Partial Task Processing with Data Slice Errors

ABSTRACT

A storage network receives data and a corresponding task, selects a storage units for the task, determines whether the data slice is locally available and when the data slice is not locally available, determines whether a redundant data slice is available from another storage unit. When the redundant data slice is not available from another storage unit, the storage network facilitates rebuilding the data slice to produce a rebuilt data slice by retrieving a decode threshold number of data slices corresponding to the data slice, decoding the decode threshold number of data slices to reproduce a data segment and re-encoding the data segment to produce a pillar width number of data slices that includes the rebuilt data slice. The storage network then stores locally either the rebuilt data slice or the redundant data slice and processes one of: the data slice locally available, the rebuilt data slice stored locally, or the redundant data slice stored locally in accordance with the corresponding partial task to produce a partial result.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 120, as a continuation of U.S. Utility application Ser. No.17/039,433, entitled “PARTIAL TASK PROCESSING WITH SLICE ERRORS, filedSep. 30, 2020, which is a continuation of U.S. Utility application Ser.No. 16/045,850, entitled “MAPPING SLICE GROUPINGS IN A DISPERSED STORAGENETWORK”, filed Jul. 26, 2018, issued as U.S. Pat. No. 10,795,766 onOct. 6, 2020, which is a continuation-in-part (CIP) of U.S. Utilitypatent application Ser. No. 15/193,335, entitled “ENCRYPTING DATA FORSTORAGE IN A DISPERSED STORAGE NETWORK,” filed Jun. 27, 2016, issued asU.S. Pat. No. 10,042,703 on Aug. 7, 2018, which is a continuation ofU.S. Utility application Ser. No. 13/868,311, entitled “ENCRYPTING DATAFOR STORAGE IN A DISPERSED STORAGE NETWORK”, filed Apr. 23, 2013, issuedas U.S. Pat. No. 9,380,032 on Jun. 28, 2016, which claims prioritypursuant to 35 U.S.C. § 119(e) to U.S. Provisional Application No.61/637,940, entitled “DATA PROCESSING BY A DISTRIBUTED STORAGE AND TASKEXECUTION UNIT”, filed Apr. 25, 2012, all of which are herebyincorporated herein by reference in their entirety and made part of thepresent U.S. Utility Patent Application for all purposes.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersed storage of data and distributed taskprocessing of data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), workstations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 5 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present invention;

FIG. 6 is a schematic block diagram of an embodiment of a dispersederror encoding in accordance with the present invention;

FIG. 7 is a diagram of an example of a segment processing of thedispersed error encoding in accordance with the present invention;

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding in accordance with thepresent invention;

FIG. 9 is a diagram of an example of grouping selection processing ofthe outbound DST processing in accordance with the present invention;

FIG. 10 is a diagram of an example of converting data into slice groupsin accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a DST executionunit in accordance with the present invention;

FIG. 12 is a schematic block diagram of an example of operation of a DSTexecution unit in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 14 is a logic diagram of an example of a method for inbound DSTprocessing in accordance with the present invention;

FIG. 15 is a diagram of an example of de-grouping selection processingof the inbound DST processing in accordance with the present invention;

FIG. 16 is a schematic block diagram of an embodiment of a dispersederror decoding in accordance with the present invention;

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of the dispersed error decoding in accordance with thepresent invention;

FIG. 18 is a diagram of an example of a de-segment processing of thedispersed error decoding in accordance with the present invention;

FIG. 19 is a diagram of an example of converting slice groups into datain accordance with the present invention;

FIG. 20 is a diagram of an example of a distributed storage within thedistributed computing system in accordance with the present invention;

FIG. 21 is a schematic block diagram of an example of operation ofoutbound distributed storage and/or task (DST) processing for storingdata in accordance with the present invention;

FIG. 22 is a schematic block diagram of an example of a dispersed errorencoding for the example of FIG. 21 in accordance with the presentinvention;

FIG. 23 is a diagram of an example of converting data into pillar slicegroups for storage in accordance with the present invention;

FIG. 24 is a schematic block diagram of an example of a storageoperation of a DST execution unit in accordance with the presentinvention;

FIG. 25 is a schematic block diagram of an example of operation ofinbound distributed storage and/or task (DST) processing for retrievingdispersed error encoded data in accordance with the present invention;

FIG. 26 is a schematic block diagram of an example of a dispersed errordecoding for the example of FIG. 25 in accordance with the presentinvention;

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing a plurality ofdata and a plurality of task codes in accordance with the presentinvention;

FIG. 28 is a schematic block diagram of an example of the distributedcomputing system performing tasks on stored data in accordance with thepresent invention;

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module facilitating the example of FIG. 28 in accordancewith the present invention;

FIG. 30 is a diagram of a specific example of the distributed computingsystem performing tasks on stored data in accordance with the presentinvention;

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 in accordance with the presentinvention;

FIG. 32 is a diagram of an example of DST allocation information for theexample of FIG. 30 in accordance with the present invention;

FIGS. 33-38 are schematic block diagrams of the DSTN module performingthe example of FIG. 30 in accordance with the present invention;

FIG. 39 is a diagram of an example of combining result information intofinal results for the example of FIG. 30 in accordance with the presentinvention;

FIG. 40A is a diagram illustrating manipulation of data in accordancewith the present invention;

FIG. 40B is a diagram illustrating encoding of data in accordance withthe present invention;

FIG. 40C is a flowchart illustrating an example of manipulating data inaccordance with the present invention;

FIG. 41A is a diagram illustrating an example of mapping slice groupingsto a set of distributed storage and task (DST) execution unit memoriesin accordance with the present invention;

FIG. 41B is a diagram illustrating another example of mapping slicegroupings to a set of distributed storage and task (DST) execution unitmemories in accordance with the present invention;

FIG. 41C is a flowchart illustrating an example of assigning slices andpartial tasks to distributed storage and task (DST) execution units inaccordance with the present invention;

FIG. 42A is a diagram illustrating another example of mapping slicegroupings to a set of distributed storage and task (DST) execution unitmemories in accordance with the present invention;

FIG. 42B is a flowchart illustrating another example of assigning slicesand partial tasks to distributed storage and task (DST) execution unitsin accordance with the present invention;

FIG. 42C is a flowchart illustrating an example of retrieving a slicefor partial task processing in accordance with the present invention;

FIG. 43A is a schematic block diagram of another embodiment of adistributed storage and task (DST) execution unit in accordance with thepresent invention;

FIG. 43B is a flowchart illustrating an example of prioritizing apartial task in accordance with the present invention;

FIG. 44A is a diagram illustrating another example of mapping slicegroupings to a set of distributed storage and task (DST) execution unitmemories in accordance with the present invention;

FIG. 44B is a diagram illustrating another example of mapping slicegroupings to a set of distributed storage and task (DST) execution unitmemories in accordance with the present invention;

FIG. 44C is a flowchart illustrating another example of assigning slicesand partial tasks to distributed storage and task (DST) execution unitsin accordance with the present invention;

FIG. 45A is a schematic block diagram of another embodiment of adistributed storage and task (DST) execution unit in accordance with thepresent invention;

FIG. 45B is a flowchart illustrating an example of generating an indexin accordance with the present invention;

FIG. 46 is a flowchart illustrating an example of identifying a portionof a slice groupings in accordance with the present invention;

FIG. 47A is a diagram illustrating another example of mapping slicegroupings to a set of distributed storage and task (DST) execution unitmemories in accordance with the present invention;

FIG. 47B is a flowchart illustrating an example of retrieving slices inaccordance with the present invention;

FIG. 48A is a schematic block diagram of an encoder system in accordancewith the present invention;

FIG. 48B is a schematic block diagram of a dispersed storage system inaccordance with the present invention;

FIG. 48C is a flowchart illustrating an example of encrypting slices inaccordance with the present invention;

FIG. 48D is a schematic block diagram of a DST execution unit with adecoder function in accordance with the present invention;

FIG. 48E is a flowchart illustrating an example of decrypting slices inaccordance with the present invention;

FIG. 49A is a diagram illustrating an example of identifying storedchunks in accordance with the present invention;

FIG. 49B is a diagram illustrating an example of a chunk storagelocation table in accordance with the present invention;

FIG. 49C is a flowchart illustrating an example of processing a partialtask request in accordance with the present invention;

FIG. 50A is a schematic block diagram of another embodiment of adistributed storage and task (DST) execution unit in accordance with thepresent invention;

FIG. 50B is a flowchart illustrating another example of processing apartial task request in accordance with the present invention; and

FIG. 50C is a flowchart illustrating another example of processing apartial task request in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system 10 that includes a user device 12 and/or a user device14, a distributed storage and/or task (DST) processing unit 16, adistributed storage and/or task network (DSTN) managing unit 18, a DSTintegrity processing unit 20, and a distributed storage and/or tasknetwork (DSTN) module 22. The components of the distributed computingsystem 10 are coupled via a network 24, which may include one or morewireless and/or wire lined communication systems; one or more privateintranet systems and/or public internet systems; and/or one or morelocal area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 that may be located at geographicallydifferent sites (e.g., one in Chicago, one in Milwaukee, etc.). Each ofthe DST execution units is operable to store dispersed error encodeddata and/or to execute, in a distributed manner, one or more tasks ondata. The tasks may be a simple function (e.g., a mathematical function,a logic function, an identify function, a find function, a search enginefunction, a replace function, etc.), a complex function (e.g.,compression, human and/or computer language translation, text-to-voiceconversion, voice-to-text conversion, etc.), multiple simple and/orcomplex functions, one or more algorithms, one or more applications,etc.

Each of the user devices 12-14, the DST processing unit 16, the DSTNmanaging unit 18, and the DST integrity processing unit 20 include acomputing core 26 and may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a personal digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core. A fixedcomputing device may be a personal computer (PC), a computer server, acable set-top box, a satellite receiver, a television set, a printer, afax machine, home entertainment equipment, a video game console, and/orany type of home or office computing equipment. User device 12 and DSTprocessing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interface 30supports a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between user device 14 and the DST processingunit 16. As another example, interface 32 supports communication links(e.g., a wired connection, a wireless connection, a LAN connection,and/or any other type of connection to/from the network 24) between userdevice 12 and the DSTN module 22 and between the DST processing unit 16and the DSTN module 22. As yet another example, interface 33 supports acommunication link for each of the DSTN managing unit 18 and DSTintegrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general, and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval (an example of which will bediscussed with reference to FIGS. 20-26 ), and data storage integrityverification. In accordance with these three primary functions, data canbe encoded, distributedly stored in physically different locations, andsubsequently retrieved in a reliable and secure manner. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width minus adecode threshold minus one) that may result from individual storagedevice failures and/or network equipment failures without loss of dataand without the need for a redundant or backup copy. Further, the systemallows the data to be stored for an indefinite period of time withoutdata loss and does so in a secure manner (e.g., the system is veryresistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has data 40 to store in the DSTN module22, it sends the data 40 to the DST processing unit 16 via its interface30. The interface 30 functions to mimic a conventional operating system(OS) file system interface (e.g., network file system (NFS), flash filesystem (FFS), disk file system (DFS), file transfer protocol (FTP),web-based distributed authoring and versioning (WebDAV), etc.) and/or ablock memory interface (e.g., small computer system interface (SCSI),internet small computer system interface (iSCSI), etc.). In addition,the interface 30 may attach a user identification code (ID) to the data40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a user device12-14 individually or as part of a group of user devices. For example,the DSTN managing unit 18 coordinates creation of a vault (e.g., avirtual memory block) within memory of the DSTN module 22 for a userdevice, a group of devices, or for public access and establishes pervault dispersed storage (DS) error encoding parameters for a vault. TheDSTN managing unit 18 may facilitate storage of DS error encodingparameters for each vault of a plurality of vaults by updating registryinformation for the distributed computing system 10. The facilitatingincludes storing updated registry information in one or more of the DSTNmodule 22, the user device 12, the DST processing unit 16, and the DSTintegrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters) include data segmenting information (e.g., how manysegments data (e.g., a file, a group of files, a data block, etc.) isdivided into), segment security information (e.g., per segmentencryption, compression, integrity checksum, etc.), error codinginformation (e.g., pillar width, decode threshold, read threshold, writethreshold, etc.), slicing information (e.g., the number of encoded dataslices that will be created for each data segment); and slice securityinformation (e.g., per encoded data slice encryption, compression,integrity checksum, etc.).

The DSTN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a private vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations includes authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials for userdevices, adding/deleting components (e.g., user devices, DST executionunits, and/or DST processing units) from the distributed computingsystem 10, and/or establishing authentication credentials for DSTexecution units 36. Network administration includes monitoring devicesand/or units for failures, maintaining vault information, determiningdevice and/or unit activation status, determining device and/or unitloading, and/or determining any other system level operation thataffects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices. At a high level, theDST integrity processing unit 20 performs rebuilding by periodicallyattempting to retrieve/list encoded data slices, and/or slice names ofthe encoded data slices, from the DSTN module 22. For retrieved encodedslices, they are checked for errors due to data corruption, outdatedversion, etc. If a slice includes an error, it is flagged as a ‘bad’slice. For encoded data slices that were not received and/or not listed,they are flagged as missing slices. Bad and/or missing slices aresubsequently rebuilt using other retrieved encoded data slices that aredeemed to be good slices to produce rebuilt slices. The rebuilt slicesare stored in memory of the DSTN module 22. Note that the DST integrityprocessing unit 20 may be a separate unit as shown, it may be includedin the DSTN module 22, it may be included in the DST processing unit 16,and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data(an example of which will be discussed with reference to FIGS. 3-19 ).With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a user device 12-14individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsinclude, but are not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials foruser devices, adding/deleting components (e.g., user devices, DSTexecution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of user device14 has a task request 38 for execution by the DSTN module 22, it sendsthe task request 38 to the DST processing unit 16 via its interface 30.An example of DST execution on stored data will be discussed in greaterdetail with reference to FIGS. 27-39 . With respect to the DSTmanagement, it is substantially similar to the DST management to supportdistributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (TO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the user device 14 of FIG. 1 . Furthernote that the IO device interface module 62 and/or the memory interfacemodules may be collectively or individually referred to as IO ports.

FIG. 3 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in user device 14 and/or in DSTprocessing unit 16 of FIG. 1 ), a network 24, a plurality of DSTexecution units 1-n that includes two or more DST execution units 36 ofFIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1 ), aDST managing module (not shown), and a DST integrity verification module(not shown). The DST client module 34 includes an outbound DSTprocessing section 80 and an inbound DST processing section 82. Each ofthe DST execution units 1-n includes a controller 86, a processingmodule 84, memory 88, a DT (distributed task) execution module 90, and aDST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92may be of any size and of any content, where, due to the size (e.g.,greater than a few Terabytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terabytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 92 and the task(s) 94. The outbound DST processingsection 80 processes the data 92 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 92 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error encodes the data partition to produce encoded data slices andgroups the encoded data slices into a slice grouping 96. In addition,the outbound DST processing section 80 partitions the task 94 intopartial tasks 98, where the number of partial tasks 98 may correspond tothe number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24,the slice groupings 96 and the partial tasks 98 to the DST executionunits 1-n of the DSTN module 22 of FIG. 1 . For example, the outboundDST processing section 80 sends slice group 1 and partial task 1 to DSTexecution unit 1. As another example, the outbound DST processingsection 80 sends slice group #n and partial task #n to DST executionunit #n.

Each DST execution unit performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit#1 performs partial task #1 on slice group #1 to produce a partialresult #1, for results. As a more specific example, slice group #1corresponds to a data partition of a series of digital books and thepartial task #1 corresponds to searching for specific phrases, recordingwhere the phrase is found, and establishing a phrase count. In this morespecific example, the partial result #1 includes information as to wherethe phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, theDST execution units send, via the network 24, their partial results 102to the inbound DST processing section 82 of the DST client module 34.The inbound DST processing section 82 processes the received partialresults 102 to produce a result 104. Continuing with the specificexample of the preceding paragraph, the inbound DST processing section82 combines the phrase count from each of the DST execution units 36 toproduce a total phrase count. In addition, the inbound DST processingsection 82 combines the ‘where the phrase was found’ information fromeach of the DST execution units 36 within their respective datapartitions to produce ‘where the phrase was found’ information for theseries of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units 36(e.g., memory of the DSTN module). In this example, the task 94 isretrieve data stored in the memory of the DSTN module. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 1-n.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 36 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit #1 receives partialtask #1 and retrieves, in response thereto, retrieved slices #1. The DSTexecution units 36 send their respective retrieved slices 100 to theinbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DST processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., aplurality of n DST execution units 36) via a network 24. The outboundDST processing section 80 includes a data partitioning module 110, adispersed storage (DS) error encoding module 112, a grouping selectormodule 114, a control module 116, and a distributed task control module118.

In an example of operation, the data partitioning module 110 partitionsdata 92 into a plurality of data partitions 120. The number ofpartitions and the size of the partitions may be selected by the controlmodule 116 via control 160 based on the data 92 (e.g., its size, itscontent, etc.), a corresponding task 94 to be performed (e.g., simple,complex, single step, multiple steps, etc.), DS encoding parameters(e.g., pillar width, decode threshold, write threshold, segment securityparameters, slice security parameters, etc.), capabilities of the DSTexecution units 36 (e.g., processing resources, availability ofprocessing recourses, etc.), and/or as may be inputted by a user, systemadministrator, or other operator (human or automated). For example, thedata partitioning module 110 partitions the data 92 (e.g., 100Terabytes) into 100,000 data segments, each being 1 Gigabyte in size.Alternatively, the data partitioning module 110 partitions the data 92into a plurality of data segments, where some of data segments are of adifferent size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in aserial manner, a parallel manner, and/or a combination thereof. For eachdata partition 120, the DS error encoding module 112 DS error encodesthe data partition 120 in accordance with control information 160 fromthe control module 116 to produce encoded data slices 122. The DS errorencoding includes segmenting the data partition into data segments,segment security processing (e.g., encryption, compression,watermarking, integrity check (e.g., CRC), etc.), error encoding,slicing, and/or per slice security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.). Thecontrol information 160 indicates which steps of the DS error encodingare active for a given data partition and, for active steps, indicatesthe parameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 122 of a datapartition into a set of slice groupings 96. The number of slicegroupings corresponds to the number of DST execution units 36 identifiedfor a particular task 94. For example, if five DST execution units 36are identified for the particular task 94, the grouping selector modulegroups the encoded slices 122 of a data partition into five slicegroupings 96. The grouping selector module 114 outputs the slicegroupings 96 to the corresponding DST execution units 36 via the network24.

The distributed task control module 118 receives the task 94 andconverts the task 94 into a set of partial tasks 98. For example, thedistributed task control module 118 receives a task to find where in thedata (e.g., a series of books) a phrase occurs and a total count of thephrase usage in the data. In this example, the distributed task controlmodule 118 replicates the task 94 for each DST execution unit 36 toproduce the partial tasks 98. In another example, the distributed taskcontrol module 118 receives a task to find where in the data a firstphrase occurs, where in the data a second phrase occurs, and a totalcount for each phrase usage in the data. In this example, thedistributed task control module 118 generates a first set of partialtasks 98 for finding and counting the first phrase and a second set ofpartial tasks for finding and counting the second phrase. Thedistributed task control module 118 sends respective first and/or secondpartial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbounddistributed storage and task (DST) processing that begins at step 126where a DST client module receives data and one or more correspondingtasks. The method continues at step 128 where the DST client moduledetermines a number of DST units to support the task for one or moredata partitions. For example, the DST client module may determine thenumber of DST units to support the task based on the size of the data,the requested task, the content of the data, a predetermined number(e.g., user indicated, system administrator determined, etc.), availableDST units, capability of the DST units, and/or any other factorregarding distributed task processing of the data. The DST client modulemay select the same DST units for each data partition, may selectdifferent DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determinesprocessing parameters of the data based on the number of DST unitsselected for distributed task processing. The processing parametersinclude data partitioning information, DS encoding parameters, and/orslice grouping information. The data partitioning information includes anumber of data partitions, size of each data partition, and/ororganization of the data partitions (e.g., number of data blocks in apartition, the size of the data blocks, and arrangement of the datablocks). The DS encoding parameters include segmenting information,segment security information, error encoding information (e.g.,dispersed storage error encoding function parameters including one ormore of pillar width, decode threshold, write threshold, read threshold,generator matrix), slicing information, and/or per slice securityinformation. The slice grouping information includes informationregarding how to arrange the encoded data slices into groups for theselected DST units. As a specific example, if the DST client moduledetermines that five DST units are needed to support the task, then itdetermines that the error encoding parameters include a pillar width offive and a decode threshold of three.

The method continues at step 132 where the DST client module determinestask partitioning information (e.g., how to partition the tasks) basedon the selected DST units and data processing parameters. The dataprocessing parameters include the processing parameters and DST unitcapability information. The DST unit capability information includes thenumber of DT (distributed task) execution units, execution capabilitiesof each DT execution unit (e.g., MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.)), and/or any information germane toexecuting one or more tasks.

The method continues at step 134 where the DST client module processesthe data in accordance with the processing parameters to produce slicegroupings. The method continues at step 136 where the DST client modulepartitions the task based on the task partitioning information toproduce a set of partial tasks. The method continues at step 138 wherethe DST client module sends the slice groupings and the correspondingpartial tasks to respective DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersedstorage (DS) error encoding module 112 of an outbound distributedstorage and task (DST) processing section.

The DS error encoding module 112 includes a segment processing module142, a segment security processing module 144, an error encoding module146, a slicing module 148, and a per slice security processing module150. Each of these modules is coupled to a control module 116 to receivecontrol information 160 therefrom.

In an example of operation, the segment processing module 142 receives adata partition 120 from a data partitioning module and receivessegmenting information as the control information 160 from the controlmodule 116. The segmenting information indicates how the segmentprocessing module 142 is to segment the data partition 120. For example,the segmenting information indicates how many rows to segment the databased on a decode threshold of an error encoding scheme, indicates howmany columns to segment the data into based on a number and size of datablocks within the data partition 120, and indicates how many columns toinclude in a data segment 152. The segment processing module 142segments the data 120 into data segments 152 in accordance with thesegmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., cyclic redundancy check(CRC), etc.), and/or any other type of digital security. For example,when the segment security processing module 144 is enabled, it maycompress a data segment 152, encrypt the compressed data segment, andgenerate a CRC value for the encrypted data segment to produce a securedata segment 154. When the segment security processing module 144 is notenabled, it passes the data segments 152 to the error encoding module146 or is bypassed such that the data segments 152 are provided to theerror encoding module 146.

The error encoding module 146 encodes the secure data segments 154 inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters (e.g., also referred to as dispersed storage errorcoding parameters) include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an online coding algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction encodingparameters identify a specific error correction encoding scheme,specifies a pillar width of five, and specifies a decode threshold ofthree. From these parameters, the error encoding module 146 encodes adata segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordancewith the pillar width of the error correction encoding parametersreceived as control information 160. For example, if the pillar width isfive, the slicing module 148 slices an encoded data segment 156 into aset of five encoded data slices. As such, for a plurality of datasegments 156 for a given data partition, the slicing module outputs aplurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice 158 based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it compresses anencoded data slice 158, encrypts the compressed encoded data slice, andgenerates a CRC value for the encrypted encoded data slice to produce asecure encoded data slice 122. When the per slice security processingmodule 150 is not enabled, it passes the encoded data slices 158 or isbypassed such that the encoded data slices 158 are the output of the DSerror encoding module 112. Note that the control module 116 may beomitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersedstorage (DS) error encoding module. In this example, a segmentprocessing module 142 receives a data partition 120 that includes 45data blocks (e.g., d1-d45), receives segmenting information (i.e.,control information 160) from a control module, and segments the datapartition 120 in accordance with the control information 160 to producedata segments 152. Each data block may be of the same size as other datablocks or of a different size. In addition, the size of each data blockmay be a few bytes to megabytes of data. As previously mentioned, thesegmenting information indicates how many rows to segment the datapartition into, indicates how many columns to segment the data partitioninto, and indicates how many columns to include in a data segment.

In this example, the decode threshold of the error encoding scheme isthree; as such the number of rows to divide the data partition into isthree. The number of columns for each row is set to 15, which is basedon the number and size of data blocks. The data blocks of the datapartition are arranged in rows and columns in a sequential order (i.e.,the first row includes the first 15 data blocks; the second row includesthe second 15 data blocks; and the third row includes the last 15 datablocks).

With the data blocks arranged into the desired sequential order, theyare divided into data segments based on the segmenting information. Inthis example, the data partition is divided into 8 data segments; thefirst 7 include 2 columns of three rows and the last includes 1 columnof three rows. Note that the first row of the 8 data segments is insequential order of the first 15 data blocks; the second row of the 8data segments in sequential order of the second 15 data blocks; and thethird row of the 8 data segments in sequential order of the last 15 datablocks. Note that the number of data blocks, the grouping of the datablocks into segments, and size of the data blocks may vary toaccommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding processing the data segmentsof FIG. 7 . In this example, data segment 1 includes 3 rows with eachrow being treated as one word for encoding. As such, data segment 1includes three words for encoding: word 1 including data blocks d1 andd2, word 2 including data blocks d16 and d17, and word 3 including datablocks d31 and d32. Each of data segments 2-7 includes three words whereeach word includes two data blocks. Data segment 8 includes three wordswhere each word includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148convert each data segment into a set of encoded data slices inaccordance with error correction encoding parameters as controlinformation 160. More specifically, when the error correction encodingparameters indicate a unity matrix Reed-Solomon based encodingalgorithm, 5 pillars, and decode threshold of 3, the first three encodeddata slices of the set of encoded data slices for a data segment aresubstantially similar to the corresponding word of the data segment. Forinstance, when the unity matrix Reed-Solomon based encoding algorithm isapplied to data segment 1, the content of the first encoded data slice(DS1_d1&2) of the first set of encoded data slices (e.g., correspondingto data segment 1) is substantially similar to content of the first word(e.g., d1 & d2); the content of the second encoded data slice(DS1_d16&17) of the first set of encoded data slices is substantiallysimilar to content of the second word (e.g., d16 & d17); and the contentof the third encoded data slice (DS1_d31&32) of the first set of encodeddata slices is substantially similar to content of the third word (e.g.,d31 & d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the first set of encoded data slices include error correctiondata based on the first-third words of the first data segment. With suchan encoding and slicing scheme, retrieving any three of the five encodeddata slices allows the data segment to be accurately reconstructed.

The encoding and slicing of data segments 2-7 yield sets of encoded dataslices similar to the set of encoded data slices of data segment 1. Forinstance, the content of the first encoded data slice (DS2_d3&4) of thesecond set of encoded data slices (e.g., corresponding to data segment2) is substantially similar to content of the first word (e.g., d3 &d4); the content of the second encoded data slice (DS2_d18&19) of thesecond set of encoded data slices is substantially similar to content ofthe second word (e.g., d18 & d19); and the content of the third encodeddata slice (DS2_d33&34) of the second set of encoded data slices issubstantially similar to content of the third word (e.g., d33 & d34).The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the second set of encoded data slices includes errorcorrection data based on the first-third words of the second datasegment.

FIG. 9 is a diagram of an example of grouping selection processing of anoutbound distributed storage and task (DST) processing in accordancewith group selection information as control information 160 from acontrol module. Encoded slices for data partition 122 are grouped inaccordance with the control information 160 to produce slice groupings96. In this example, a grouping selector module 114 organizes theencoded data slices into five slice groupings (e.g., one for each DSTexecution unit of a distributed storage and task network (DSTN) module).As a specific example, the grouping selector module 114 creates a firstslice grouping for a DST execution unit #1, which includes first encodedslices of each of the sets of encoded slices. As such, the first DSTexecution unit receives encoded data slices corresponding to data blocks1-15 (e.g., encoded data slices of contiguous data).

The grouping selector module 114 also creates a second slice groupingfor a DST execution unit #2, which includes second encoded slices ofeach of the sets of encoded slices. As such, the second DST executionunit receives encoded data slices corresponding to data blocks 16-30.The grouping selector module 114 further creates a third slice groupingfor DST execution unit #3, which includes third encoded slices of eachof the sets of encoded slices. As such, the third DST execution unitreceives encoded data slices corresponding to data blocks 31-45.

The grouping selector module 114 creates a fourth slice grouping for DSTexecution unit #4, which includes fourth encoded slices of each of thesets of encoded slices. As such, the fourth DST execution unit receivesencoded data slices corresponding to first error encoding information(e.g., encoded data slices of error coding (EC) data). The groupingselector module 114 further creates a fifth slice grouping for DSTexecution unit #5, which includes fifth encoded slices of each of thesets of encoded slices. As such, the fifth DST execution unit receivesencoded data slices corresponding to second error encoding information.

FIG. 10 is a diagram of an example of converting data 92 into slicegroups that expands on the preceding figures. As shown, the data 92 ispartitioned in accordance with a partitioning function 164 into aplurality of data partitions (1-x, where x is an integer greater than4). Each data partition (or chunkset of data) is encoded and groupedinto slice groupings as previously discussed by an encoding and groupingfunction 166. For a given data partition, the slice groupings are sentto distributed storage and task (DST) execution units. From datapartition to data partition, the ordering of the slice groupings to theDST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DSTexecution units such that the first DST execution receives first encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a first continuous data chunk of the first data partition(e.g., refer to FIG. 9 ), a second DST execution receives second encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a second continuous data chunk of the first datapartition, etc.

For the second data partition, the slice groupings may be sent to theDST execution units in a different order than it was done for the firstdata partition. For instance, the first slice grouping of the seconddata partition (e.g., slice group 2_1) is sent to the second DSTexecution unit; the second slice grouping of the second data partition(e.g., slice group 2_2) is sent to the third DST execution unit; thethird slice grouping of the second data partition (e.g., slice group2_3) is sent to the fourth DST execution unit; the fourth slice groupingof the second data partition (e.g., slice group 2_4, which includesfirst error coding information) is sent to the fifth DST execution unit;and the fifth slice grouping of the second data partition (e.g., slicegroup 2_5, which includes second error coding information) is sent tothe first DST execution unit.

The pattern of sending the slice groupings to the set of DST executionunits may vary in a predicted pattern, a random pattern, and/or acombination thereof from data partition to data partition. In addition,from data partition to data partition, the set of DST execution unitsmay change. For example, for the first data partition, DST executionunits 1-5 may be used; for the second data partition, DST executionunits 6-10 may be used; for the third data partition, DST executionunits 3-7 may be used; etc. As is also shown, the task is divided intopartial tasks that are sent to the DST execution units in conjunctionwith the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST(distributed storage and/or task) execution unit that includes aninterface 169, a controller 86, memory 88, one or more DT (distributedtask) execution modules 90, and a DST client module 34. The memory 88 isof sufficient size to store a significant number of encoded data slices(e.g., thousands of slices to hundreds-of-millions of slices) and mayinclude one or more hard drives and/or one or more solid-state memorydevices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution modulereceives a slice grouping 96 (e.g., slice group #1) via interface 169.The slice grouping 96 includes, per partition, encoded data slices ofcontiguous data or encoded data slices of error coding (EC) data. Forslice group #1, the DST execution module receives encoded data slices ofcontiguous data for partitions #1 and #x (and potentially others between3 and x) and receives encoded data slices of EC data for partitions #2and #3 (and potentially others between 3 and x). Examples of encodeddata slices of contiguous data and encoded data slices of error coding(EC) data are discussed with reference to FIG. 9 . The memory 88 storesthe encoded data slices of slice groupings 96 in accordance with memorycontrol information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates thememory control information 174 based on a partial task(s) 98 anddistributed computing information (e.g., user information (e.g., userID, distributed computing permissions, data access permission, etc.),vault information (e.g., virtual memory assigned to user, user group,temporary storage for task processing, etc.), task validationinformation, etc.). For example, the controller 86 interprets thepartial task(s) 98 in light of the distributed computing information todetermine whether a requestor is authorized to perform the task 98, isauthorized to access the data, and/or is authorized to perform the taskon this particular data. When the requestor is authorized, thecontroller 86 determines, based on the task 98 and/or another input,whether the encoded data slices of the slice grouping 96 are to betemporarily stored or permanently stored. Based on the foregoing, thecontroller 86 generates the memory control information 174 to write theencoded data slices of the slice grouping 96 into the memory 88 and toindicate whether the slice grouping 96 is permanently stored ortemporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86facilitates execution of the partial task(s) 98. In an example, thecontroller 86 interprets the partial task 98 in light of thecapabilities of the DT execution module(s) 90. The capabilities includeone or more of MIPS capabilities, processing resources (e.g., quantityand capability of microprocessors, CPUs, digital signal processors,co-processor, microcontrollers, arithmetic logic circuitry, and/or anyother analog and/or digital processing circuitry), availability of theprocessing resources, etc. If the controller 86 determines that the DTexecution module(s) 90 have sufficient capabilities, it generates taskcontrol information 176.

The task control information 176 may be a generic instruction (e.g.,perform the task on the stored slice grouping) or a series ofoperational codes. In the former instance, the DT execution module 90includes a co-processor function specifically configured (fixed orprogrammed) to perform the desired task 98. In the latter instance, theDT execution module 90 includes a general processor topology where thecontroller stores an algorithm corresponding to the particular task 98.In this instance, the controller 86 provides the operational codes(e.g., assembly language, source code of a programming language, objectcode, etc.) of the algorithm to the DT execution module 90 forexecution.

Depending on the nature of the task 98, the DT execution module 90 maygenerate intermediate partial results 102 that are stored in the memory88 or in a cache memory (not shown) within the DT execution module 90.In either case, when the DT execution module 90 completes execution ofthe partial task 98, it outputs one or more partial results 102. Thepartial results 102 may also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of theDT execution module(s) 90 can support the partial task 98, thecontroller 86 determines that the DT execution module(s) 90 cannotadequately support the task 98 (e.g., does not have the right resources,does not have sufficient available resources, available resources wouldbe too slow, etc.), it then determines whether the partial task 98should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fullyoffloaded, it generates DST control information 178 and provides it tothe DST client module 34. The DST control information 178 includes thepartial task 98, memory storage information regarding the slice grouping96, and distribution instructions. The distribution instructionsinstruct the DST client module 34 to divide the partial task 98 intosub-partial tasks 172, to divide the slice grouping 96 into sub-slicegroupings 170, and identify other DST execution units. The DST clientmodule 34 functions in a similar manner as the DST client module 34 ofFIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slicegroupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partialresults), via the interface 169, from the DST execution units to whichthe task was offloaded. The DST client module 34 provides thesub-partial results to the DST execution unit, which processes thesub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should bepartially offloaded, it determines what portion of the task 98 and/orslice grouping 96 should be processed locally and what should beoffloaded. For the portion that is being locally processed, thecontroller 86 generates task control information 176 as previouslydiscussed. For the portion that is being offloaded, the controller 86generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g.,sub-partial results) from the DST executions units to which a portion ofthe task was offloaded, it provides the sub-partial results to the DTexecution module 90. The DT execution module 90 processes thesub-partial results with the sub-partial results it created to producethe partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of storedslices 100, stored results 104, partial results 102 when the DTexecution module 90 stores partial results 102 and/or results 104 in thememory 88. For example, when the partial task 98 includes a retrievalrequest, the controller 86 outputs the memory control 174 to the memory88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of adistributed storage and task (DST) execution unit storing encoded dataslices and executing a task thereon. To store the encoded data slices ofa partition 1 of slice grouping 1, a controller 86 generates writecommands as memory control information 174 such that the encoded slicesare stored in desired locations (e.g., permanent or temporary) withinmemory 88.

Once the encoded slices are stored, the controller 86 provides taskcontrol information 176 to a distributed task (DT) execution module 90.As a first step of executing the task in accordance with the taskcontrol information 176, the DT execution module 90 retrieves theencoded slices from memory 88. The DT execution module 90 thenreconstructs contiguous data blocks of a data partition. As shown forthis example, reconstructed contiguous data blocks of data partition 1include data blocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module90 performs the task on the reconstructed contiguous data blocks. Forexample, the task may be to search the reconstructed contiguous datablocks for a particular word or phrase, identify where in thereconstructed contiguous data blocks the particular word or phraseoccurred, and/or count the occurrences of the particular word or phraseon the reconstructed contiguous data blocks. The DST execution unitcontinues in a similar manner for the encoded data slices of otherpartitions in slice grouping 1. Note that with using the unity matrixerror encoding scheme previously discussed, if the encoded data slicesof contiguous data are uncorrupted, the decoding of them is a relativelystraightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (ormissing), it can be rebuilt by accessing other DST execution units thatare storing the other encoded data slices of the set of encoded dataslices of the corrupted encoded data slice. In this instance, the DSTexecution unit having the corrupted encoded data slices retrieves atleast three encoded data slices (of contiguous data and of error codingdata) in the set from the other DST execution units (recall for thisexample, the pillar width is 5 and the decode threshold is 3). The DSTexecution unit decodes the retrieved data slices using the DS errorencoding parameters to recapture the corresponding data segment. The DSTexecution unit then re-encodes the data segment using the DS errorencoding parameters to rebuild the corrupted encoded data slice. Oncethe encoded data slice is rebuilt, the DST execution unit functions aspreviously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing section 82 of a DSTclient module coupled to DST execution units of a distributed storageand task network (DSTN) module via a network 24. The inbound DSTprocessing section 82 includes a de-grouping module 180, a DS (dispersedstorage) error decoding module 182, a data de-partitioning module 184, acontrol module 186, and a distributed task control module 188. Note thatthe control module 186 and/or the distributed task control module 188may be separate modules from corresponding ones of outbound DSTprocessing section or may be the same modules.

In an example of operation, the DST execution units have completedexecution of corresponding partial tasks on the corresponding slicegroupings to produce partial results 102. The inbound DST processingsection 82 receives the partial results 102 via the distributed taskcontrol module 188. The inbound DST processing section 82 then processesthe partial results 102 to produce a final result, or results 104. Forexample, if the task were to find a specific word or phrase within data,the partial results 102 indicate where in each of the prescribedportions of the data the corresponding DST execution units found thespecific word or phrase. The distributed task control module 188combines the individual partial results 102 for the correspondingportions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82is retrieving stored data from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices 100 corresponding to the data retrieval requests. The de-groupingmodule 180 receives retrieved slices 100 and de-groups them to produceencoded data slices per data partition 122. The DS error decoding module182 decodes, in accordance with DS error encoding parameters, theencoded data slices per data partition 122 to produce data partitions120.

The data de-partitioning module 184 combines the data partitions 120into the data 92. The control module 186 controls the conversion ofretrieved slices 100 into the data 92 using control signals 190 to eachof the modules. For instance, the control module 186 providesde-grouping information to the de-grouping module 180, provides the DSerror encoding parameters to the DS error decoding module 182, andprovides de-partitioning information to the data de-partitioning module184.

FIG. 14 is a logic diagram of an example of a method that is executableby distributed storage and task (DST) client module regarding inboundDST processing. The method begins at step 194 where the DST clientmodule receives partial results. The method continues at step 196 wherethe DST client module retrieves the task corresponding to the partialresults. For example, the partial results include header informationthat identifies the requesting entity, which correlates to the requestedtask.

The method continues at step 198 where the DST client module determinesresult processing information based on the task. For example, if thetask were to identify a particular word or phrase within the data, theresult processing information would indicate to aggregate the partialresults for the corresponding portions of the data to produce the finalresult. As another example, if the task were to count the occurrences ofa particular word or phrase within the data, results of processing theinformation would indicate to add the partial results to produce thefinal results. The method continues at step 200 where the DST clientmodule processes the partial results in accordance with the resultprocessing information to produce the final result or results.

FIG. 15 is a diagram of an example of de-grouping selection processingof an inbound distributed storage and task (DST) processing section of aDST client module. In general, this is an inverse process of thegrouping module of the outbound DST processing section of FIG. 9 .Accordingly, for each data partition (e.g., partition #1), thede-grouping module retrieves the corresponding slice grouping from theDST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, whichincludes the first encoded slices of each of the sets of encoded slices(e.g., encoded data slices of contiguous data of data blocks 1-15); DSTexecution unit #2 provides a second slice grouping, which includes thesecond encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 16-30); DSTexecution unit #3 provides a third slice grouping, which includes thethird encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 31-45); DSTexecution unit #4 provides a fourth slice grouping, which includes thefourth encoded slices of each of the sets of encoded slices (e.g., firstencoded data slices of error coding (EC) data); and DST execution unit#5 provides a fifth slice grouping, which includes the fifth encodedslices of each of the sets of encoded slices (e.g., first encoded dataslices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., receivedslices 100) using a de-grouping selector 180 controlled by a controlsignal 190 as shown in the example to produce a plurality of sets ofencoded data slices (e.g., retrieved slices for a partition into sets ofslices 122). Each set corresponding to a data segment of the datapartition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186, unsecures eachencoded data slice 122 based on slice de-security information receivedas control information 190 (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6 ) received from thecontrol module 186. The slice security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRCverification, etc.), and/or any other type of digital security. Forexample, when the inverse per slice security processing module 202 isenabled, it verifies integrity information (e.g., a CRC value) of eachencoded data slice 122, it decrypts each verified encoded data slice,and decompresses each decrypted encoded data slice to produce sliceencoded data 158. When the inverse per slice security processing module202 is not enabled, it passes the encoded data slices 122 as the slicedencoded data 158 or is bypassed such that the retrieved encoded dataslices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 intoencoded data segments 156 in accordance with a pillar width of the errorcorrection encoding parameters received as control information 190 fromthe control module 186. For example, if the pillar width is five, thede-slicing module 204 de-slices a set of five encoded data slices intoan encoded data segment 156. The error decoding module 206 decodes theencoded data segments 156 in accordance with error correction decodingparameters received as control information 190 from the control module186 to produce secure data segments 154. The error correction decodingparameters include identifying an error correction encoding scheme(e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction decoding parameters identify a specific errorcorrection encoding scheme, specify a pillar width of five, and specifya decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments 154 based onsegment security information received as control information 190 fromthe control module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module 208 isenabled, it verifies integrity information (e.g., a CRC value) of eachsecure data segment 154, it decrypts each verified secured data segment,and decompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 154 as the data segment152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 andreceives de-segmenting information as control information 190 from thecontrol module 186. The de-segmenting information indicates how thede-segment processing module 210 is to de-segment the data segments 152into a data partition 120. For example, the de-segmenting informationindicates how the rows and columns of data segments are to be rearrangedto yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of a dispersed error decoding module. A de-slicing module 204receives at least a decode threshold number of encoded data slices 158for each data segment in accordance with control information 190 andprovides encoded data 156. In this example, a decode threshold is three.As such, each set of encoded data slices 158 is shown to have threeencoded data slices per data segment. The de-slicing module 204 mayreceive three encoded data slices per data segment because an associateddistributed storage and task (DST) client module requested retrievingonly three encoded data slices per segment or selected three of theretrieved encoded data slices per data segment. As shown, which is basedon the unity matrix encoding previously discussed with reference to FIG.8 , an encoded data slice may be a data-based encoded data slice (e.g.,DS1_d1&d2) or an error code based encoded data slice (e.g., ES3_1).

An error decoding module 206 decodes the encoded data 156 of each datasegment in accordance with the error correction decoding parameters ofcontrol information 190 to produce secured segments 154. In thisexample, data segment 1 includes 3 rows with each row being treated asone word for encoding. As such, data segment 1 includes three words:word 1 including data blocks d1 and d2, word 2 including data blocks d16and d17, and word 3 including data blocks d31 and d32. Each of datasegments 2-7 includes three words where each word includes two datablocks. Data segment 8 includes three words where each word includes asingle data block (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of a de-segment processing of aninbound distributed storage and task (DST) processing. In this example,a de-segment processing module 210 receives data segments 152 (e.g.,1-8) and rearranges the data blocks of the data segments into rows andcolumns in accordance with de-segmenting information of controlinformation 190 to produce a data partition 120. Note that the number ofrows is based on the decode threshold (e.g., 3 in this specific example)and the number of columns is based on the number and size of the datablocks.

The de-segmenting module 210 converts the rows and columns of datablocks into the data partition 120. Note that each data block may be ofthe same size as other data blocks or of a different size. In addition,the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data92 within an inbound distributed storage and task (DST) processingsection. As shown, the data 92 is reconstructed from a plurality of datapartitions (1-x, where x is an integer greater than 4). Each datapartition (or chunk set of data) is decoded and re-grouped using ade-grouping and decoding function 212 and a de-partition function 214from slice groupings as previously discussed. For a given datapartition, the slice groupings (e.g., at least a decode threshold perdata segment of encoded data slices) are received from DST executionunits. From data partition to data partition, the ordering of the slicegroupings received from the DST execution units may vary as discussedwith reference to FIG. 10 .

FIG. 20 is a diagram of an example of a distributed storage and/orretrieval within the distributed computing system. The distributedcomputing system includes a plurality of distributed storage and/or task(DST) processing client modules 34 (one shown) coupled to a distributedstorage and/or task processing network (DSTN) module, or multiple DSTNmodules, via a network 24. The DST client module 34 includes an outboundDST processing section 80 and an inbound DST processing section 82. TheDSTN module includes a plurality of DST execution units. Each DSTexecution unit includes a controller 86, memory 88, one or moredistributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 thatit desires to store in the DSTN module. The data 92 may be a file (e.g.,video, audio, text, graphics, etc.), a data object, a data block, anupdate to a file, an update to a data block, etc. In this instance, theoutbound DST processing module 80 converts the data 92 into encoded dataslices 216 as will be further described with reference to FIGS. 21-23 .The outbound DST processing module 80 sends, via the network 24, to theDST execution units for storage as further described with reference toFIG. 24 .

In an example of data retrieval, the DST client module 34 issues aretrieve request to the DST execution units for the desired data 92. Theretrieve request may address each DST executions units storing encodeddata slices of the desired data, address a decode threshold number ofDST execution units, address a read threshold number of DST executionunits, or address some other number of DST execution units. In responseto the request, each addressed DST execution unit retrieves its encodeddata slices 100 of the desired data and sends them to the inbound DSTprocessing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82receives at least a decode threshold number of encoded data slices 100,it converts the encoded data slices 100 into a data segment. The inboundDST processing section 82 aggregates the data segments to produce theretrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module coupled to a distributed storage and task network (DSTN)module (e.g., a plurality of DST execution units) via a network 24. Theoutbound DST processing section 80 includes a data partitioning module110, a dispersed storage (DS) error encoding module 112, a groupingselector module 114, a control module 116, and a distributed taskcontrol module 118.

In an example of operation, the data partitioning module 110 isby-passed such that data 92 is provided directly to the DS errorencoding module 112. The control module 116 coordinates the by-passingof the data partitioning module 110 by outputting a bypass 220 messageto the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serialmanner, a parallel manner, and/or a combination thereof. The DS errorencoding module 112 DS error encodes the data in accordance with controlinformation 160 from the control module 116 to produce encoded dataslices 218. The DS error encoding includes segmenting the data 92 intodata segments, segment security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC, etc.)), errorencoding, slicing, and/or per slice security processing (e.g.,encryption, compression, watermarking, integrity check (e.g., CRC,etc.)). The control information 160 indicates which steps of the DSerror encoding are active for the data 92 and, for active steps,indicates the parameters for the step. For example, the controlinformation 160 indicates that the error encoding is active and includeserror encoding parameters (e.g., pillar width, decode threshold, writethreshold, read threshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 218 of thedata segments into pillars of slices 216. The number of pillarscorresponds to the pillar width of the DS error encoding parameters. Inthis example, the distributed task control module 118 facilitates thestorage request.

FIG. 22 is a schematic block diagram of an example of a dispersedstorage (DS) error encoding module 112 for the example of FIG. 21 . TheDS error encoding module 112 includes a segment processing module 142, asegment security processing module 144, an error encoding module 146, aslicing module 148, and a per slice security processing module 150. Eachof these modules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receivesdata 92 and receives segmenting information as control information 160from the control module 116. The segmenting information indicates howthe segment processing module is to segment the data. For example, thesegmenting information indicates the size of each data segment. Thesegment processing module 142 segments the data 92 into data segments152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., CRC, etc.), and/or anyother type of digital security. For example, when the segment securityprocessing module 144 is enabled, it compresses a data segment 152,encrypts the compressed data segment, and generates a CRC value for theencrypted data segment to produce a secure data segment. When thesegment security processing module 144 is not enabled, it passes thedata segments 152 to the error encoding module 146 or is bypassed suchthat the data segments 152 are provided to the error encoding module146.

The error encoding module 146 encodes the secure data segments inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction encoding parameters identify a specific errorcorrection encoding scheme, specifies a pillar width of five, andspecifies a decode threshold of three. From these parameters, the errorencoding module 146 encodes a data segment to produce an encoded datasegment.

The slicing module 148 slices the encoded data segment in accordancewith a pillar width of the error correction encoding parameters. Forexample, if the pillar width is five, the slicing module slices anencoded data segment into a set of five encoded data slices. As such,for a plurality of data segments, the slicing module 148 outputs aplurality of sets of encoded data slices as shown within encoding andslicing function 222 as described.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it may compress anencoded data slice, encrypt the compressed encoded data slice, andgenerate a CRC value for the encrypted encoded data slice to produce asecure encoded data slice tweaking. When the per slice securityprocessing module 150 is not enabled, it passes the encoded data slicesor is bypassed such that the encoded data slices 218 are the output ofthe DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillarslice groups utilizing encoding, slicing and pillar grouping function224 for storage in memory of a distributed storage and task network(DSTN) module. As previously discussed the data 92 is encoded and slicedinto a plurality of sets of encoded data slices; one set per datasegment. The grouping selector module organizes the sets of encoded dataslices into pillars of data slices. In this example, the DS errorencoding parameters include a pillar width of 5 and a decode thresholdof 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selector module takes the first encoded data slice of eachof the sets and forms a first pillar, which may be sent to the first DSTexecution unit. Similarly, the grouping selector module creates thesecond pillar from the second slices of the sets; the third pillar fromthe third slices of the sets; the fourth pillar from the fourth slicesof the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributedstorage and/or task (DST) execution unit that includes an interface 169,a controller 86, memory 88, one or more distributed task (DT) executionmodules 90, and a DST client module 34. A computing core 26 may beutilized to implement the one or more DT execution modules 90 and theDST client module 34. The memory 88 is of sufficient size to store asignificant number of encoded data slices (e.g., thousands of slices tohundreds-of-millions of slices) and may include one or more hard drivesand/or one or more solid-state memory devices (e.g., flash memory, DRAM,etc.).

In an example of storing a pillar of slices 216, the DST execution unitreceives, via interface 169, a pillar of slices 216 (e.g., pillar #1slices). The memory 88 stores the encoded data slices 216 of the pillarof slices in accordance with memory control information 174 it receivesfrom the controller 86. The controller 86 (e.g., a processing module, aCPU, etc.) generates the memory control information 174 based ondistributed storage information (e.g., user information (e.g., user ID,distributed storage permissions, data access permission, etc.), vaultinformation (e.g., virtual memory assigned to user, user group, etc.),etc.). Similarly, when retrieving slices, the DST execution unitreceives, via interface 169, a slice retrieval request. The memory 88retrieves the slice in accordance with memory control information 174 itreceives from the controller 86. The memory 88 outputs the slice 100,via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of aninbound distributed storage and/or task (DST) processing section 82 forretrieving dispersed error encoded data 92. The inbound DST processingsection 82 includes a de-grouping module 180, a dispersed storage (DS)error decoding module 182, a data de-partitioning module 184, a controlmodule 186, and a distributed task control module 188. Note that thecontrol module 186 and/or the distributed task control module 188 may beseparate modules from corresponding ones of an outbound DST processingsection or may be the same modules.

In an example of operation, the inbound DST processing section 82 isretrieving stored data 92 from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices corresponding to data retrieval requests from the distributedtask control module 188. The de-grouping module 180 receives pillars ofslices 100 and de-groups them in accordance with control information 190from the control module 186 to produce sets of encoded data slices 218.The DS error decoding module 182 decodes, in accordance with the DSerror encoding parameters received as control information 190 from thecontrol module 186, each set of encoded data slices 218 to produce datasegments, which are aggregated into retrieved data 92. The datade-partitioning module 184 is by-passed in this operational mode via abypass signal 226 of control information 190 from the control module186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, and a de-segmenting processing module 210. Thedispersed error decoding module 182 is operable to de-slice and decodeencoded slices per data segment 218 utilizing a de-slicing and decodingfunction 228 to produce a plurality of data segments that arede-segmented utilizing a de-segment function 230 to recover data 92.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186 via controlinformation 190, unsecures each encoded data slice 218 based on slicede-security information (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6 ) received as controlinformation 190 from the control module 186. The slice de-securityinformation includes data decompression, decryption, de-watermarking,integrity check (e.g., CRC verification, etc.), and/or any other type ofdigital security. For example, when the inverse per slice securityprocessing module 202 is enabled, it verifies integrity information(e.g., a CRC value) of each encoded data slice 218, it decrypts eachverified encoded data slice, and decompresses each decrypted encodeddata slice to produce slice encoded data. When the inverse per slicesecurity processing module 202 is not enabled, it passes the encodeddata slices 218 as the sliced encoded data or is bypassed such that theretrieved encoded data slices 218 are provided as the sliced encodeddata.

The de-slicing module 204 de-slices the sliced encoded data into encodeddata segments in accordance with a pillar width of the error correctionencoding parameters received as control information 190 from a controlmodule 186. For example, if the pillar width is five, the de-slicingmodule de-slices a set of five encoded data slices into an encoded datasegment. Alternatively, the encoded data segment may include just threeencoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments inaccordance with error correction decoding parameters received as controlinformation 190 from the control module 186 to produce secure datasegments. The error correction decoding parameters include identifyingan error correction encoding scheme (e.g., forward error correctionalgorithm, a Reed-Solomon based algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction decodingparameters identify a specific error correction encoding scheme, specifya pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments based on segmentsecurity information received as control information 190 from thecontrol module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module is enabled,it verifies integrity information (e.g., a CRC value) of each securedata segment, it decrypts each verified secured data segment, anddecompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 152 as the data segmentor is bypassed. The de-segmenting processing module 210 aggregates thedata segments 152 into the data 92 in accordance with controlinformation 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module that includes aplurality of distributed storage and task (DST) execution units (#1through #n, where, for example, n is an integer greater than or equal tothree). Each of the DST execution units includes a DST client module 34,a controller 86, one or more DT (distributed task) execution modules 90,and memory 88.

In this example, the DSTN module stores, in the memory of the DSTexecution units, a plurality of DS (dispersed storage) encoded data(e.g., 1 through n, where n is an integer greater than or equal to two)and stores a plurality of DS encoded task codes (e.g., 1 through k,where k is an integer greater than or equal to two). The DS encoded datamay be encoded in accordance with one or more examples described withreference to FIGS. 3-19 (e.g., organized in slice groupings) or encodedin accordance with one or more examples described with reference toFIGS. 20-26 (e.g., organized in pillar groups). The data that is encodedinto the DS encoded data may be of any size and/or of any content. Forexample, the data may be one or more digital books, a copy of acompany's emails, a large-scale Internet search, a video security file,one or more entertainment video files (e.g., television programs,movies, etc.), data files, and/or any other large amount of data (e.g.,greater than a few Terabytes).

The tasks that are encoded into the DS encoded task code may be a simplefunction (e.g., a mathematical function, a logic function, an identifyfunction, a find function, a search engine function, a replace function,etc.), a complex function (e.g., compression, human and/or computerlanguage translation, text-to-voice conversion, voice-to-textconversion, etc.), multiple simple and/or complex functions, one or morealgorithms, one or more applications, etc. The tasks may be encoded intothe DS encoded task code in accordance with one or more examplesdescribed with reference to FIGS. 3-19 (e.g., organized in slicegroupings) or encoded in accordance with one or more examples describedwith reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of aDST processing unit issues a DST request to the DSTN module. The DSTrequest may include a request to retrieve stored data, or a portionthereof, may include a request to store data that is included with theDST request, may include a request to perform one or more tasks onstored data, may include a request to perform one or more tasks on dataincluded with the DST request, etc. In the cases where the DST requestincludes a request to store data or to retrieve data, the client moduleand/or the DSTN module processes the request as previously discussedwith reference to one or more of FIGS. 3-19 (e.g., slice groupings)and/or 20-26 (e.g., pillar groupings). In the case where the DST requestincludes a request to perform one or more tasks on data included withthe DST request, the DST client module and/or the DSTN module processthe DST request as previously discussed with reference to one or more ofFIGS. 3-19 .

In the case where the DST request includes a request to perform one ormore tasks on stored data, the DST client module and/or the DSTN moduleprocesses the DST request as will be described with reference to one ormore of FIGS. 28-39 . In general, the DST client module identifies dataand one or more tasks for the DSTN module to execute upon the identifieddata. The DST request may be for a one-time execution of the task or foran on-going execution of the task. As an example of the latter, as acompany generates daily emails, the DST request may be to daily searchnew emails for inappropriate content and, if found, record the content,the email sender(s), the email recipient(s), email routing information,notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributedcomputing system performing tasks on stored data. In this example, twodistributed storage and task (DST) client modules 1-2 are shown: thefirst may be associated with a user device and the second may beassociated with a DST processing unit or a high priority user device(e.g., high priority clearance user, system administrator, etc.). EachDST client module includes a list of stored data 234 and a list of taskscodes 236. The list of stored data 234 includes one or more entries ofdata identifying information, where each entry identifies data stored inthe DSTN module 22. The data identifying information (e.g., data ID)includes one or more of a data file name, a data file directory listing,DSTN addressing information of the data, a data object identifier, etc.The list of tasks 236 includes one or more entries of task codeidentifying information, when each entry identifies task codes stored inthe DSTN module 22. The task code identifying information (e.g., taskID) includes one or more of a task file name, a task file directorylisting, DSTN addressing information of the task, another type ofidentifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are eachsmaller in number of entries for the first DST client module than thecorresponding lists of the second DST client module. This may occurbecause the user device associated with the first DST client module hasfewer privileges in the distributed computing system than the deviceassociated with the second DST client module. Alternatively, this mayoccur because the user device associated with the first DST clientmodule serves fewer users than the device associated with the second DSTclient module and is restricted by the distributed computing systemaccordingly. As yet another alternative, this may occur through norestraints by the distributed computing system, it just occurred becausethe operator of the user device associated with the first DST clientmodule has selected fewer data and/or fewer tasks than the operator ofthe device associated with the second DST client module.

In an example of operation, the first DST client module selects one ormore data entries 238 and one or more tasks 240 from its respectivelists (e.g., selected data ID and selected task ID). The first DSTclient module sends its selections to a task distribution module 232.The task distribution module 232 may be within a stand-alone device ofthe distributed computing system, may be within the user device thatcontains the first DST client module, or may be within the DSTN module22.

Regardless of the task distribution module's location, it generates DSTallocation information 242 from the selected task ID 240 and theselected data ID 238. The DST allocation information 242 includes datapartitioning information, task execution information, and/orintermediate result information. The task distribution module 232 sendsthe DST allocation information 242 to the DSTN module 22. Note that oneor more examples of the DST allocation information will be discussedwith reference to one or more of FIGS. 29-39 .

The DSTN module 22 interprets the DST allocation information 242 toidentify the stored DS encoded data (e.g., DS error encoded data 2) andto identify the stored DS error encoded task code (e.g., DS errorencoded task code 1). In addition, the DSTN module 22 interprets the DSTallocation information 242 to determine how the data is to bepartitioned and how the task is to be partitioned. The DSTN module 22also determines whether the selected DS error encoded data 238 needs tobe converted from pillar grouping to slice grouping. If so, the DSTNmodule 22 converts the selected DS error encoded data into slicegroupings and stores the slice grouping DS error encoded data byoverwriting the pillar grouping DS error encoded data or by storing itin a different location in the memory of the DSTN module 22 (i.e., doesnot overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in theDST allocation information 242 and sends the portions to selected DSTexecution units of the DSTN module 22. Each of the selected DSTexecution units performs its partial task(s) on its slice groupings toproduce partial results. The DSTN module 22 collects the partial resultsfrom the selected DST execution units and provides them, as resultinformation 244, to the task distribution module. The result information244 may be the collected partial results, one or more final results asproduced by the DSTN module 22 from processing the partial results inaccordance with the DST allocation information 242, or one or moreintermediate results as produced by the DSTN module 22 from processingthe partial results in accordance with the DST allocation information242.

The task distribution module 232 receives the result information 244 andprovides one or more final results 104 therefrom to the first DST clientmodule. The final result(s) 104 may be result information 244 or aresult(s) of the task distribution module's processing of the resultinformation 244.

In concurrence with processing the selected task of the first DST clientmodule, the distributed computing system may process the selectedtask(s) of the second DST client module on the selected data(s) of thesecond DST client module. Alternatively, the distributed computingsystem may process the second DST client module's request subsequent to,or preceding, that of the first DST client module. Regardless of theordering and/or parallel processing of the DST client module requests,the second DST client module provides its selected data 238 and selectedtask 240 to a task distribution module 232. If the task distributionmodule 232 is a separate device of the distributed computing system orwithin the DSTN module, the task distribution modules 232 coupled to thefirst and second DST client modules may be the same module. The taskdistribution module 232 processes the request of the second DST clientmodule in a similar manner as it processed the request of the first DSTclient module.

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module 232 facilitating the example of FIG. 28 . The taskdistribution module 232 includes a plurality of tables it uses togenerate distributed storage and task (DST) allocation information 242for selected data and selected tasks received from a DST client module.The tables include data storage information 248, task storageinformation 250, distributed task (DT) execution module information 252,and task⇔sub-task mapping information 246.

The data storage information table 248 includes a data identification(ID) field 260, a data size field 262, an addressing information field264, distributed storage (DS) information 266, and may further includeother information regarding the data, how it is stored, and/or how itcan be processed. For example, DS encoded data #1 has a data ID of 1, adata size of AA (e.g., a byte size of a few Terabytes or more),addressing information of Addr_1_AA, and DS parameters of 3/5; SEG_1;and SLC_1. In this example, the addressing information may be a virtualaddress corresponding to the virtual address of the first storage word(e.g., one or more bytes) of the data and information on how tocalculate the other addresses, may be a range of virtual addresses forthe storage words of the data, physical addresses of the first storageword or the storage words of the data, may be a list of slices names ofthe encoded data slices of the data, etc. The DS parameters may includeidentity of an error encoding scheme, decode threshold/pillar width(e.g., 3/5 for the first data entry), segment security information(e.g., SEG_1), per slice security information (e.g., SLC_1), and/or anyother information regarding how the data was encoded into data slices.

The task storage information table 250 includes a task identification(ID) field 268, a task size field 270, an addressing information field272, distributed storage (DS) information 274, and may further includeother information regarding the task, how it is stored, and/or how itcan be used to process data. For example, DS encoded task #2 has a taskID of 2, a task size of XY, addressing information of Addr_2_XY, and DSparameters of 3/5; SEG_2; and SLC_2. In this example, the addressinginformation may be a virtual address corresponding to the virtualaddress of the first storage word (e.g., one or more bytes) of the taskand information on how to calculate the other addresses, may be a rangeof virtual addresses for the storage words of the task, physicaladdresses of the first storage word or the storage words of the task,may be a list of slices names of the encoded slices of the task code,etc. The DS parameters may include identity of an error encoding scheme,decode threshold/pillar width (e.g., 3/5 for the first data entry),segment security information (e.g., SEG_2), per slice securityinformation (e.g., SLC_2), and/or any other information regarding howthe task was encoded into encoded task slices. Note that the segmentand/or the per-slice security information include a type of encryption(if enabled), a type of compression (if enabled), watermarkinginformation (if enabled), and/or an integrity check scheme (if enabled).

The task⇔sub-task mapping information table 246 includes a task field256 and a sub-task field 258. The task field 256 identifies a taskstored in the memory of a distributed storage and task network (DSTN)module and the corresponding sub-task fields 258 indicates whether thetask includes sub-tasks and, if so, how many and if any of the sub-tasksare ordered. In this example, the task⇔sub-task mapping informationtable 246 includes an entry for each task stored in memory of the DSTNmodule (e.g., task 1 through task k). In particular, this exampleindicates that task 1 includes 7 sub-tasks; task 2 does not includesub-tasks, and task k includes r number of sub-tasks (where r is aninteger greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field276, a DT execution module ID field 278, and a DT execution modulecapabilities field 280. The DST execution unit ID field 276 includes theidentity of DST units in the DSTN module. The DT execution module IDfield 278 includes the identity of each DT execution unit in each DSTunit. For example, DST unit 1 includes three DT executions modules(e.g., 1_1, 1_2, and 1_3). The DT execution capabilities field 280includes identity of the capabilities of the corresponding DT executionunit. For example, DT execution module 1_1 includes capabilities X,where X includes one or more of MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.), and/or any information germane toexecuting one or more tasks.

From these tables, the task distribution module 232 generates the DSTallocation information 242 to indicate where the data is stored, how topartition the data, where the task is stored, how to partition the task,which DT execution units should perform which partial task on which datapartitions, where and how intermediate results are to be stored, etc. Ifmultiple tasks are being performed on the same data or different data,the task distribution module factors such information into itsgeneration of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computingsystem performing tasks on stored data as a task flow 318. In thisexample, selected data 92 is data 2 and selected tasks are tasks 1, 2,and 3. Task 1 corresponds to analyzing translation of data from onelanguage to another (e.g., human language or computer language); task 2corresponds to finding specific words and/or phrases in the data; andtask 3 corresponds to finding specific translated words and/or phrasesin translated data.

In this example, task 1 includes 7 sub-tasks: task 1_1—identifynon-words (non-ordered); task 1_2—identify unique words (non-ordered);task 1_3—translate (non-ordered); task 1_4—translate back (ordered aftertask 1_3); task 1_5—compare to ID errors (ordered after task 1-4); task1_6—determine non-word translation errors (ordered after task 1_5 and1_1); and task 1_7—determine correct translations (ordered after 1_5 and1_2). The sub-task further indicates whether they are an ordered task(i.e., are dependent on the outcome of another task) or non-order (i.e.,are independent of the outcome of another task). Task 2 does not includesub-tasks and task 3 includes two sub-tasks: task 3_1 translate; andtask 3_2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze datafor translation accuracies, translation errors, translation anomalies,occurrence of specific words or phrases in the data, and occurrence ofspecific words or phrases on the translated data. Graphically, the data92 is translated 306 into translated data 282; is analyzed for specificwords and/or phrases 300 to produce a list of specific words and/orphrases 286; is analyzed for non-words 302 (e.g., not in a referencedictionary) to produce a list of non-words 290; and is analyzed forunique words 316 included in the data 92 (i.e., how many different wordsare included in the data) to produce a list of unique words 298. Each ofthese tasks is independent of each other and can therefore be processedin parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_2) for specifictranslated words and/or phrases 304 to produce a list of specifictranslated words and/or phrases 288. The translated data 282 istranslated back 308 (e.g., sub-task 1_4) into the language of theoriginal data to produce re-translated data 284. These two tasks aredependent on the translate task (e.g., task 1_3) and thus must beordered after the translation task, which may be in a pipelined orderingor a serial ordering. The re-translated data 284 is then compared 310with the original data 92 to find words and/or phrases that did nottranslate (one way and/or the other) properly to produce a list ofincorrectly translated words 294. As such, the comparing task (e.g.,sub-task 1_5) 310 is ordered after the translation 306 andre-translation tasks 308 (e.g., sub-tasks 1_3 and 1_4).

The list of words incorrectly translated 294 is compared 312 to the listof non-words 290 to identify words that were not properly translatedbecause the words are non-words to produce a list of errors due tonon-words 292. In addition, the list of words incorrectly translated 294is compared 314 to the list of unique words 298 to identify unique wordsthat were properly translated to produce a list of correctly translatedwords 296. The comparison may also identify unique words that were notproperly translated to produce a list of unique words that were notproperly translated. Note that each list of words (e.g., specific wordsand/or phrases, non-words, unique words, translated words and/orphrases, etc.,) may include the word and/or phrase, how many times it isused, where in the data it is used, and/or any other informationrequested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 . As shown, DS encoded data 2 is storedas encoded data slices across the memory (e.g., stored in memories 88)of DST execution units 1-5; the DS encoded task code 1 (of task 1) andDS encoded task 3 are stored as encoded task slices across the memory ofDST execution units 1-5; and DS encoded task code 2 (of task 2) isstored as encoded task slices across the memory of DST execution units3-7. As indicated in the data storage information table and the taskstorage information table of FIG. 29 , the respective data/task has DSparameters of 3/5 for their decode threshold/pillar width; hencespanning the memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST)allocation information 242 for the example of FIG. 30 . The DSTallocation information 242 includes data partitioning information 320,task execution information 322, and intermediate result information 324.The data partitioning information 320 includes the data identifier (ID),the number of partitions to split the data into, address information foreach data partition, and whether the DS encoded data has to betransformed from pillar grouping to slice grouping. The task executioninformation 322 includes tabular information having a taskidentification field 326, a task ordering field 328, a data partitionfield ID 330, and a set of DT execution modules 332 to use for thedistributed task processing per data partition. The intermediate resultinformation 324 includes tabular information having a name ID field 334,an ID of the DST execution unit assigned to process the correspondingintermediate result 336, a scratch pad storage field 338, and anintermediate result storage field 340.

Continuing with the example of FIG. 30 , where tasks 1-3 are to bedistributedly performed on data 2, the data partitioning informationincludes the ID of data 2. In addition, the task distribution moduledetermines whether the DS encoded data 2 is in the proper format fordistributed computing (e.g., was stored as slice groupings). If not, thetask distribution module indicates that the DS encoded data 2 formatneeds to be changed from the pillar grouping format to the slicegrouping format, which will be done by the DSTN module. In addition, thetask distribution module determines the number of partitions to dividethe data into (e.g., 2_1 through 2_z) and addressing information foreach partition.

The task distribution module generates an entry in the task executioninformation section for each sub-task to be performed. For example, task1_1 (e.g., identify non-words on the data) has no task ordering (i.e.,is independent of the results of other sub-tasks), is to be performed ondata partitions 2_1 through 2_z by DT execution modules 1_1, 2_1, 3_1,4_1, and 5_1. For instance, DT execution modules 1_1, 2_1, 3_1, 4_1, and5_1 search for non-words in data partitions 2_1 through 2_z to producetask 1_1 intermediate results (R1-1, which is a list of non-words). Task1_2 (e.g., identify unique words) has similar task execution informationas task 1_1 to produce task 1_2 intermediate results (R1-2, which is thelist of unique words).

Task 1_3 (e.g., translate) includes task execution information as beingnon-ordered (i.e., is independent), having DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 andhaving DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 translate datapartitions 2-5 through 2_z to produce task 1_3 intermediate results(R1-3, which is the translated data). In this example, the datapartitions are grouped, where different sets of DT execution modulesperform a distributed sub-task (or task) on each data partition group,which allows for further parallel processing.

Task 1_4 (e.g., translate back) is ordered after task 1_3 and is to beexecuted on task 1_3's intermediate result (e.g., R1-3_1) (e.g., thetranslated data). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 areallocated to translate back task 1_3 intermediate result partitionsR1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and7_2 are allocated to translate back task 1 3 intermediate resultpartitions R1-3 5 through R1-3_z to produce task 1-4 intermediateresults (R1-4, which is the translated back data).

Task 1_5 (e.g., compare data and translated data to identify translationerrors) is ordered after task 1_4 and is to be executed on task 1_4'sintermediate results (R4-1) and on the data. DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 are allocated to compare the data partitions (2_1through 2_z) with partitions of task 1-4 intermediate results partitionsR1-4_1 through R1-4_z to produce task 1_5 intermediate results (R1-5,which is the list words translated incorrectly).

Task 1_6 (e.g., determine non-word translation errors) is ordered aftertasks 1_1 and 1_5 and is to be executed on tasks 1_1's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_1, 2_1,3_1, 4_1, and 5_1 are allocated to compare the partitions of task 1_1intermediate results (R1-1_1 through R1-1_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_6 intermediate results (R1-6, which is the list translation errors dueto non-words).

Task 1_7 (e.g., determine words correctly translated) is ordered aftertasks 1_2 and 1_5 and is to be executed on tasks 1_2's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_2, 2_2,3_2, 4_2, and 5_2 are allocated to compare the partitions of task 1_2intermediate results (R1-2_1 through R1-2_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_7 intermediate results (R1-7, which is the list of correctlytranslated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering(i.e., is independent of the results of other sub-tasks), is to beperformed on data partitions 2_1 through 2_z by DT execution modules3_1, 4_1, 5_1, 6_1, and 7_1. For instance, DT execution modules 3_1,4_1, 5_1, 6_1, and 7_1 search for specific words and/or phrases in datapartitions 2_1 through 2_z to produce task 2 intermediate results (R2,which is a list of specific words and/or phrases).

Task 3_2 (e.g., find specific translated words and/or phrases) isordered after task 1_3 (e.g., translate) is to be performed onpartitions R1-3_1 through R1-3_z by DT execution modules 1_2, 2_2, 3_2,4_2, and 5_2. For instance, DT execution modules 1_2, 2_2, 3_2, 4_2, and5_2 search for specific translated words and/or phrases in thepartitions of the translated data (R1-3_1 through R1-3_z) to producetask 3_2 intermediate results (R3-2, which is a list of specifictranslated words and/or phrases).

For each task, the intermediate result information indicates which DSTunit is responsible for overseeing execution of the task and, if needed,processing the partial results generated by the set of allocated DTexecution units. In addition, the intermediate result informationindicates a scratch pad memory for the task and where the correspondingintermediate results are to be stored. For example, for intermediateresult R1-1 (the intermediate result of task 1_1), DST unit 1 isresponsible for overseeing execution of the task 1_1 and coordinatesstorage of the intermediate result as encoded intermediate result slicesstored in memory of DST execution units 1-5. In general, the scratch padis for storing non-DS encoded intermediate results and the intermediateresult storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage andtask network (DSTN) module performing the example of FIG. 30 . In FIG.33 , the DSTN module accesses the data 92 and partitions it into aplurality of partitions 1-z in accordance with distributed storage andtask network (DST) allocation information. For each data partition, theDSTN identifies a set of its DT (distributed task) execution modules 90to perform the task (e.g., identify non-words (i.e., not in a referencedictionary) within the data partition) in accordance with the DSTallocation information. From data partition to data partition, the setof DT execution modules 90 may be the same, different, or a combinationthereof (e.g., some data partitions use the same set while other datapartitions use different sets).

For the first data partition, the first set of DT execution modules(e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information ofFIG. 32 ) executes task 1_1 to produce a first partial result 102 ofnon-words found in the first data partition. The second set of DTexecution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DSTallocation information of FIG. 32 ) executes task 1_1 to produce asecond partial result 102 of non-words found in the second datapartition. The sets of DT execution modules (as per the DST allocationinformation) perform task 1_1 on the data partitions until the “z” setof DT execution modules performs task 1_1 on the “zth” data partition toproduce a “zth” partial result 102 of non-words found in the “zth” datapartition.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 1 is assigned to process the first through “zth” partialresults to produce the first intermediate result (R1-1), which is a listof non-words found in the data. For instance, each set of DT executionmodules 90 stores its respective partial result in the scratchpad memoryof DST execution unit 1 (which is identified in the DST allocation ormay be determined by DST execution unit 1). A processing module of DSTexecution 1 is engaged to aggregate the first through “zth” partialresults to produce the first intermediate result (e.g., R1_1). Theprocessing module stores the first intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slicegrouping-based DS error encode the first intermediate result (e.g., thelist of non-words). To begin the encoding, the DST client moduledetermines whether the list of non-words is of a sufficient size topartition (e.g., greater than a Terra-Byte). If yes, it partitions thefirst intermediate result (R1-1) into a plurality of partitions (e.g.,R1-1_1 through R1-1_m). If the first intermediate result is not ofsufficient size to partition, it is not partitioned.

For each partition of the first intermediate result, or for the firstintermediate result, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 34 , the DSTN module is performing task 1_2 (e.g., find uniquewords) on the data 92. To begin, the DSTN module accesses the data 92and partitions it into a plurality of partitions 1-z in accordance withthe DST allocation information or it may use the data partitions of task1_1 if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_2 inaccordance with the DST allocation information. From data partition todata partition, the set of DT execution modules may be the same,different, or a combination thereof. For the data partitions, theallocated set of DT execution modules executes task 1_2 to produce apartial results (e.g., 1^(st) through “zth”) of unique words found inthe data partitions.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 1 is assigned to process the first through “zth” partialresults 102 of task 1_2 to produce the second intermediate result(R1-2), which is a list of unique words found in the data 92. Theprocessing module of DST execution 1 is engaged to aggregate the firstthrough “zth” partial results of unique words to produce the secondintermediate result. The processing module stores the secondintermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slicegrouping-based DS error encode the second intermediate result (e.g., thelist of non-words). To begin the encoding, the DST client moduledetermines whether the list of unique words is of a sufficient size topartition (e.g., greater than a Terra-Byte). If yes, it partitions thesecond intermediate result (R1-2) into a plurality of partitions (e.g.,R1-2_1 through R1-2_m). If the second intermediate result is not ofsufficient size to partition, it is not partitioned.

For each partition of the second intermediate result, or for the secondintermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 35 , the DSTN module is performing task 1_3 (e.g., translate) onthe data 92. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions 1-z in accordance with theDST allocation information or it may use the data partitions of task 1_1if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_3 inaccordance with the DST allocation information (e.g., DT executionmodules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1through 2_4 and DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2translate data partitions 2_5 through 2_z). For the data partitions, theallocated set of DT execution modules 90 executes task 1_3 to producepartial results 102 (e.g., 1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 2 is assigned to process the first through “zth” partialresults of task 1_3 to produce the third intermediate result (R1-3),which is translated data. The processing module of DST execution 2 isengaged to aggregate the first through “zth” partial results oftranslated data to produce the third intermediate result. The processingmodule stores the third intermediate result as non-DS error encoded datain the scratchpad memory or in another section of memory of DSTexecution unit 2.

DST execution unit 2 engages its DST client module to slicegrouping-based DS error encode the third intermediate result (e.g.,translated data). To begin the encoding, the DST client modulepartitions the third intermediate result (R1-3) into a plurality ofpartitions (e.g., R1-3_1 through R1-3_y). For each partition of thethird intermediate result, the DST client module uses the DS errorencoding parameters of the data (e.g., DS parameters of data 2, whichincludes 3/5 decode threshold/pillar width ratio) to produce slicegroupings. The slice groupings are stored in the intermediate resultmemory (e.g., allocated memory in the memories of DST execution units2-6 per the DST allocation information).

As is further shown in FIG. 35 , the DSTN module is performing task 1_4(e.g., retranslate) on the translated data of the third intermediateresult. To begin, the DSTN module accesses the translated data (from thescratchpad memory or from the intermediate result memory and decodes it)and partitions it into a plurality of partitions in accordance with theDST allocation information. For each partition of the third intermediateresult, the DSTN identifies a set of its DT execution modules 90 toperform task 1_4 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated totranslate back partitions R1-3_1 through R1-3_4 and DT execution modules1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back partitionsR1-3_5 through R1-3_z). For the partitions, the allocated set of DTexecution modules executes task 1_4 to produce partial results 102(e.g., 1^(st) through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 3 is assigned to process the first through “zth” partialresults of task 1_4 to produce the fourth intermediate result (R1-4),which is retranslated data. The processing module of DST execution 3 isengaged to aggregate the first through “zth” partial results ofretranslated data to produce the fourth intermediate result. Theprocessing module stores the fourth intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 3.

DST execution unit 3 engages its DST client module to slicegrouping-based DS error encode the fourth intermediate result (e.g.,retranslated data). To begin the encoding, the DST client modulepartitions the fourth intermediate result (R1-4) into a plurality ofpartitions (e.g., R1-4_1 through R1-4_z). For each partition of thefourth intermediate result, the DST client module uses the DS errorencoding parameters of the data (e.g., DS parameters of data 2, whichincludes 3/5 decode threshold/pillar width ratio) to produce slicegroupings. The slice groupings are stored in the intermediate resultmemory (e.g., allocated memory in the memories of DST execution units3-7 per the DST allocation information).

In FIG. 36 , a distributed storage and task network (DSTN) module isperforming task 1_5 (e.g., compare) on data 92 and retranslated data ofFIG. 35 . To begin, the DSTN module accesses the data 92 and partitionsit into a plurality of partitions in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. The DSTN module also accesses the retranslateddata from the scratchpad memory, or from the intermediate result memoryand decodes it, and partitions it into a plurality of partitions inaccordance with the DST allocation information. The number of partitionsof the retranslated data corresponds to the number of partitions of thedata.

For each pair of partitions (e.g., data partition 1 and retranslateddata partition 1), the DSTN identifies a set of its DT execution modules90 to perform task 1_5 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_5 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 1 is assigned to process the first through “zth” partialresults of task 1_5 to produce the fifth intermediate result (R1-5),which is the list of incorrectly translated words and/or phrases. Inparticular, the processing module of DST execution 1 is engaged toaggregate the first through “zth” partial results of the list ofincorrectly translated words and/or phrases to produce the fifthintermediate result. The processing module stores the fifth intermediateresult as non-DS error encoded data in the scratchpad memory or inanother section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slicegrouping-based DS error encode the fifth intermediate result. To beginthe encoding, the DST client module partitions the fifth intermediateresult (R1-5) into a plurality of partitions (e.g., R1-5_1 throughR1-5_z). For each partition of the fifth intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes 3/5 decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36 , the DSTN module is performing task 1_6(e.g., translation errors due to non-words) on the list of incorrectlytranslated words and/or phrases (e.g., the fifth intermediate resultR1-5) and the list of non-words (e.g., the first intermediate resultR1-1). To begin, the DSTN module accesses the lists and partitions theminto a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_6 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_6 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases due to non-words.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 2 is assigned to process the first through “zth” partialresults of task 1_6 to produce the sixth intermediate result (R1-6),which is the list of incorrectly translated words and/or phrases due tonon-words. In particular, the processing module of DST execution 2 isengaged to aggregate the first through “zth” partial results of the listof incorrectly translated words and/or phrases due to non-words toproduce the sixth intermediate result. The processing module stores thesixth intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slicegrouping-based DS error encode the sixth intermediate result. To beginthe encoding, the DST client module partitions the sixth intermediateresult (R1-6) into a plurality of partitions (e.g., R1-6_1 throughR1-6_z). For each partition of the sixth intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes 3/5 decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36 , the DSTN module is performingtask 1_7 (e.g., correctly translated words and/or phrases) on the listof incorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of unique words (e.g., the secondintermediate result R1-2). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_7 in accordance with the DST allocation information(e.g., DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2). For each pairof partitions, the allocated set of DT execution modules executes task1_7 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 3 is assigned to process the first through “zth” partialresults of task 1_7 to produce the seventh intermediate result (R1-7),which is the list of correctly translated words and/or phrases. Inparticular, the processing module of DST execution 3 is engaged toaggregate the first through “zth” partial results of the list ofcorrectly translated words and/or phrases to produce the seventhintermediate result. The processing module stores the seventhintermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 3.

DST execution unit 3 engages its DST client module to slicegrouping-based DS error encode the seventh intermediate result. To beginthe encoding, the DST client module partitions the seventh intermediateresult (R1-7) into a plurality of partitions (e.g., R1-7_1 throughR1-7_z). For each partition of the seventh intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes 3/5 decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 3-7 per the DST allocation information).

In FIG. 37 , the distributed storage and task network (DSTN) module isperforming task 2 (e.g., find specific words and/or phrases) on the data92. To begin, the DSTN module accesses the data and partitions it into aplurality of partitions 1-z in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. For each data partition, the DSTN identifies aset of its DT execution modules 90 to perform task 2 in accordance withthe DST allocation information. From data partition to data partition,the set of DT execution modules may be the same, different, or acombination thereof. For the data partitions, the allocated set of DTexecution modules executes task 2 to produce partial results 102 (e.g.,1^(st) through “zth”) of specific words and/or phrases found in the datapartitions.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 7 is assigned to process the first through “zth” partialresults of task 2 to produce task 2 intermediate result (R2), which is alist of specific words and/or phrases found in the data. The processingmodule of DST execution 7 is engaged to aggregate the first through“zth” partial results of specific words and/or phrases to produce thetask 2 intermediate result. The processing module stores the task 2intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slicegrouping-based DS error encode the task 2 intermediate result. To beginthe encoding, the DST client module determines whether the list ofspecific words and/or phrases is of a sufficient size to partition(e.g., greater than a Terra-Byte). If yes, it partitions the task 2intermediate result (R2) into a plurality of partitions (e.g., R2_1through R2_m). If the task 2 intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the task 2 intermediate result, or for the task 2intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38 , the distributed storage and task network (DSTN) module isperforming task 3 (e.g., find specific translated words and/or phrases)on the translated data (R1-3). To begin, the DSTN module accesses thetranslated data (from the scratchpad memory or from the intermediateresult memory and decodes it) and partitions it into a plurality ofpartitions in accordance with the DST allocation information. For eachpartition, the DSTN identifies a set of its DT execution modules toperform task 3 in accordance with the DST allocation information. Frompartition to partition, the set of DT execution modules may be the same,different, or a combination thereof. For the partitions, the allocatedset of DT execution modules 90 executes task 3 to produce partialresults 102 (e.g., 1^(st) through “zth”) of specific translated wordsand/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 5 is assigned to process the first through “zth” partialresults of task 3 to produce task 3 intermediate result (R3), which is alist of specific translated words and/or phrases found in the translateddata. In particular, the processing module of DST execution 5 is engagedto aggregate the first through “zth” partial results of specifictranslated words and/or phrases to produce the task 3 intermediateresult. The processing module stores the task 3 intermediate result asnon-DS error encoded data in the scratchpad memory or in another sectionof memory of DST execution unit 7.

DST execution unit 5 engages its DST client module to slicegrouping-based DS error encode the task 3 intermediate result. To beginthe encoding, the DST client module determines whether the list ofspecific translated words and/or phrases is of a sufficient size topartition (e.g., greater than a Terra-Byte). If yes, it partitions thetask 3 intermediate result (R3) into a plurality of partitions (e.g.,R3_1 through R3_m). If the task 3 intermediate result is not ofsufficient size to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information intofinal results 104 for the example of FIG. 30 . In this example, theresult information includes the list of specific words and/or phrasesfound in the data (task 2 intermediate result), the list of specifictranslated words and/or phrases found in the data (task 3 intermediateresult), the list of non-words found in the data (task 1 firstintermediate result R1-1), the list of unique words found in the data(task 1 second intermediate result R1-2), the list of translation errorsdue to non-words (task 1 sixth intermediate result R1-6), and the listof correctly translated words and/or phrases (task 1 seventhintermediate result R1-7). The task distribution module provides theresult information to the requesting DST client module as the results104.

FIG. 40A is a diagram illustrating manipulation of data 350. Themanipulation includes manipulating data 350 into one or more chunksets1-N that form a data matrix 352. The data 350 includes a plurality ofdata groups 1-15. Each data group of the plurality of data groupsincludes one or more associated bytes sharing a commonality, wherein thecommonality includes at least one of a text line, a text page, a textdocument, a video clip, a video file, an audio segment, an audio file,context, a data type, a time relationship, and a spatial relationship.For example, a data group 1 includes 12,000 bytes of a video clip. Asanother example, a data group 2 includes 15,000 bytes of a textdocument.

Each chunkset of the one or more chunksets 1-N includes a decodethreshold number of chunks such that each chunk is substantiallyidentical in size. A number of bytes per chunk may be selected inaccordance with the chunk size selection scheme. For example, the chunksize is selected to be greater than or equal to a largest data groupsize such that a largest data group associated with the largest datagroup size may fit within any chunk when the chunk size selection schemeindicates to fit any data group into any chunk. For instance, the chunksize is selected as 20 kB when a largest data group size (e.g., of datagroup 8) is 20 kB. The chunk size selection scheme enables subsequentprocessing of a partial task on any data group by processing the partialtask on a corresponding single chunk (e.g., stored in a distributedstorage and task (DST) execution unit).

The decode threshold number (e.g., number of chunks per chunkset) may bedetermined based on at least one of a desired reliability performancelevel, a predetermination, a pillar width number, the chunk size, a datapattern of the data, aligning similar data types with similar chunknumbers of resulting two or more chunksets resulting from manipulationof the data, and a request. For example, the decode threshold number isdetermined to be 5 when the pillar width number is 8 in accordance witha desired level of reliability performance. As another example, thedecode threshold number is determined to be 5 when the chunk size is 20kB and a data pattern of the data repeats every 100 kB.

One or more data groups of the plurality of data groups is packed intoeach chunk such that a size of the one or more data groups is less thanor equal to the chunk size. As such, a data group is not split by aboundary between two chunks. Unused capacity within each chunk may bepadded with pad bytes, wherein each pad byte includes at least one of apredetermined value, a random value, a value associated with a chunknumber, a value associated with a chunkset, a value associated with atleast one data group packed into the chunk, and at least one partialtask associated with the chunk. For example, a first chunk of a firstchunkset is packed with 12 kB of data group 1 and 8 kB of apredetermined pad byte value. As another example, a third chunk of thefirst chunkset is packed with 9 kB of data group 3, 7 kB of data group4, and 4 kB of pad bytes that identify chunk 3. As another example, afourth chunk of the first chunkset is packed with 9 kB of data group 5and 11 kB of data group 6 to completely fill the 20 kB chunk size.

The data matrix 352 includes the one or more chunksets 1-N, wherein eachrow of the data matrix includes a chunkset of the one or more chunksets1-N. For example, a first row of the data matrix includes the firstchunkset, wherein the first row is filled with the first chunk followedby the second chunk followed by the third chunk followed by the fourthchunk followed by the fifth chunk. The data matrix 352 may be furtherprocessed to form slice groupings as is discussed in greater detail withreference to FIG. 40B.

FIG. 40B is a diagram illustrating encoding of data that includesmanipulated data organized into a data matrix 352 as a plurality ofchunksets 1-N, a chunkset data matrix 354 for each of the plurality ofchunksets 1-N that includes a row for each chunk, a column selector 364to select a column of the chunkset data matrix 354, a data selectionmatrix 358 to hold a column of the chunkset data matrix 354, a generatormatrix 356 to encode each data selection of each chunkset to produce acorresponding chunkset slice matrix 360 of slices, and a pillar selector362 to route slices of each chunkset to a corresponding distributedstorage and task execution (DST EX) unit for task processing. A numberof chunks per chunkset is obtained from a previous data manipulationprocess. A decode threshold of an information dispersal algorithm (IDA)is determined as the number of chunks per chunkset. A pillar widthnumber of the IDA is determined based on or more of the previous datamanipulation process, the decode threshold, a number of available DST EXunits, an availability requirement, and a reliability requirement. Forexample, the pillar width is set at 8 when the decode threshold is 5 andin accordance with a reliability requirement.

A chunk size of each chunkset is obtained from the previous datamanipulation process. A chunkset size is the number of chunks perchunkset multiplied by the chunk size. For example, the chunkset size is100 k bytes when the chunk size is 20 k bytes and the number of chunksper chunkset is 5. A number of chunksets N is determined as a size ofthe data divided by the size of the chunkset.

The generator matrix 356 is determined in accordance with the IDA andincludes a decode threshold number of columns and a pillar width numberof rows. A unity matrix is utilized in a top square matrix to facilitategeneration of contiguous slices that match contiguous data of chunks.Other rows of the encoding matrix 356 facilitate generating error codedslices for remaining rows of the chunkset slice matrix 360.

For each chunkset, the generator matrix 356 is matrix multiplied by acolumn of the corresponding chunkset data matrix 354 (e.g., the dataselection 358 as selected by the column selector 364) to generate acolumn of the chunkset slice matrix 360 for the corresponding chunkset.For example, row 1 of the generator matrix 356 is multiplied by column 1of the chunkset data matrix 354 to produce a row 1 byte of column 1 ofthe chunkset slice matrix 360, row 2 of the generator matrix 356 ismultiplied by column 1 of the chunkset data matrix 354 to produce a row2 byte of column 1 of the chunkset slice matrix 360, etc. As anotherexample, row 1 of the generator matrix 356 is multiplied by column 2 ofthe chunkset data matrix 354 to produce a row 1 byte of column 2 of thechunkset slice matrix 360, row 2 of the generator matrix 356 ismultiplied by column 2 of the chunkset data matrix 354 to produce a row2 byte of column 2 of the chunkset slice matrix 360, etc.

A segment may be considered as one or more columns of the chunkset datamatrix 354 and slices that correspond to the segment are the rows of thechunkset slice matrix 360 that correspond to the one or more columns ofthe chunkset data matrix 354. For example, row 1 columns 1 and 2 of thechunkset slice matrix 360 form slice 1 when columns 1 and 2 of thechunkset data matrix 354 are considered as a corresponding segment.Slices of a common row of the chunkset slice matrix 360 are of a chunkof contiguous data of the data and share a common pillar number and maytypically be stored in a common DST EX unit to facilitate a distributedtask.

The pillar selector 362 routes slices and error coded slices of eachpillar to a DST EX unit in accordance with a pillar selection scheme.For example, two slices of row 1 (e.g., slice comprising bytes fromcolumns 1 through 10 k and slice 2 comprising bytes from columns 10 k+1through 20 k) of the chunkset slice matrix 360 are sent to DST EX unit 1as a contiguous chunk of data that includes 20 k bytes when the pillarselection scheme maps pillars 1-5 (e.g., associated with slices ofcontiguous data), to DST EX units 1-5 and maps pillars 6-8 (e.g.,associated with error coded slices) to DST EX units 6-8 for a firstchunkset.

To facilitate load leveling of tasks executed by the DST EX units, thepillar selection scheme may include rotating assignments of pillars todifferent DST EX units for each chunkset. For example, two slices of row8 (e.g., slice comprising bytes from columns 1 through 10 k and slice 2comprising bytes from columns 10 k+1 through 20 k) of the chunkset slicematrix 360 are sent to DST EX unit 1 as error coded data slices thatincludes 20 k bytes when the pillar selection scheme maps pillar 8(e.g., associated with error coded slices), to DST EX units 1 and mapspillars 1 (e.g., associated with slices of contiguous data) to DST EXunits 8 for another chunkset.

FIG. 40C is a flowchart illustrating an example of manipulating data,which include similar steps to FIG. 5 . The method begins with step 126of FIG. 5 where a processing module (e.g., of a distributed storage andtask (DST) client module) receives data and a corresponding task. Themethod continues at step 366 where the processing module selects one ormore DST execution units for the task based on a capability levelassociated with each of the DST execution units. The selecting includesone or more of determining a number of DST execution units and selectingthe number of DST execution units based on one or more of an estimateddistributed computing loading level, a DST execution unit capabilityindicator, a DST execution unit performance indicator, a DST executionunit availability level indicator, a task schedule, and a DST executionunit threshold computing capability indicator. For example, theprocessing module selects DST execution units 1-8 when DST executionunit availability level indicators for DST execution units 1-8 comparesfavorably to an estimated distributed computing loading level.

The method continues at step 368 where the processing module identifiesa plurality of data groups of the data. The identifying includes atleast one of receiving identification information, analyzing the data,and estimating the data groups based on previous data groups. The methodcontinues at step 370 where the processing module determines a chunksize based on the plurality of data groups. The determining may be basedon at least one of a chunk size selection scheme, a predetermination,and receiving the chunk size. For example, the processing moduledetermines the chunk size to be greater than or equal to a largest datagroup size such that a largest data group associated with the largestdata group size may fit within any chunk when the chunk size selectionscheme indicates to fit any data group into any chunk.

The method continues at step 372 where the processing module determinesprocessing parameters of the data based on the chunk size. Theprocessing parameters includes at least one of a decode threshold numberand a pillar width number. The processing module may determine thedecode threshold number (e.g., number of chunks per chunkset) based onat least one of a desired reliability performance level, apredetermination, a pillar width number, the chunk size, a data patternof the data, aligning similar data types with similar chunk numbers ofresulting two or more chunksets resulting from manipulation of the data,and a request. For example, the decode threshold number is determined tobe 10 when the pillar width number is 16 in accordance with a desiredlevel of reliability performance. As another example, the decodethreshold number is determined to be 5 when the chunk size is 20 kB anda data pattern of the data repeats every 100 kB.

The method continues at step 374 where the processing module generates aset of chunksets from the plurality of data groups in accordance withthe chunk size and processing parameters. The generation includespacking one or more data groups of the plurality of data groups intoeach chunk of each chunkset such that a size of the one or more datagroups is less than or equal to the chunk size. Unused capacity withineach chunk may be padded with pad bytes, wherein each pad byte includesat least one of a predetermined value, a random value, a valueassociated with a chunk number, a value associated with a chunkset, avalue associated with at least one data group packed into the chunk, andat least one partial task associated with the chunk.

The method continues at step 376 where the processing module encodes theset of chunksets in accordance with the processing parameters to produceslice groupings. The encoding includes encoding each chunkset of the setof chunksets with a dispersed storage error coded function to produce adecode threshold number of slices and a pillar width number minus thedecode threshold number of error coded slices and forming a pillar widthnumber of slice groupings that includes the slices and the error codedslices. The method continues with steps 132, 136, and 138 of FIG. 5where the processing module determines task partitioning based on theDST execution units and the processing parameters, partitions the taskbased on the task partitioning to produce partial tasks, and sends theslice groupings and corresponding partial tasks to the DST executionunits.

FIG. 41A is a diagram illustrating an example of mapping slice groupingsto a set of distributed storage and task (DST) execution unit memories.The mapping includes a slice to memory mapping for two or more DSTexecution unit memories. Slices associated with one or more chunksetsare distributed to the DST execution unit memories in accordance with aDST execution unit selection scheme. The DST execution unit selectionscheme includes at least one of selecting a same DST execution unit forslices associated with a common pillar and a round robin scheme. Slicesof a common chunk number (e.g., a common pillar number) are sent to acommon DST execution unit when the selection scheme includes selectingthe same DST execution unit for slices associated with the commonpillar. For example, slice 1,1,1, slice 2,1,1, slice 3,1,1, slice 4,1,1of chunk 1 of chunkset 1 are selected for sending to DST execution unit1, wherein slice 1,2,3 is associated with a first slice of a secondchunk of a third chunkset. As another example, slice 1,2,3, slice 2,2,3,slice3,2,3, and slice 4,2,3 are selected for sending to DST executionunit 2.

Each DST execution unit of the associated DST execution unit memoriesexecutes a partial task on each slice in accordance with an executionordering. For example, a first job runs to execute partial tasks onslices of a first chunk and a second job runs to execute partial taskson slices of a second chunk. As another example, a first job runs on afirst slice of each chunk of each chunkset and a second job runs on asecond slice of each chunk of each chunkset.

FIG. 41B is a diagram illustrating another example of mapping slicegroupings to a set of distributed storage and task (DST) execution unitmemories. The mapping includes a slice to memory mapping for two or moreDST execution unit memories. Slices associated with one or morechunksets are distributed to the DST execution unit memories inaccordance with a DST execution unit selection scheme. The DST executionunit selection scheme includes at least one of selecting a same DSTexecution unit for slices associated with a common pillar and a roundrobin scheme. Slices of a rotating chunk numbers (e.g., rotating pillarnumbers) are sent to a DST execution unit when the selection schemeincludes the round robin scheme. For example, slice 1,1,1, slice 2,1,1,slice 3,1,1, slice 4,1,1 of chunk 1 of chunkset 1 and slice 1,2,2, slice2,2,2, slice 3,2,2, and slice 4,2,2 are selected for sending to DSTexecution unit 1. In such an example, DST execution unit 1 receives afirst chunk of a first chunkset and a second chunk of a second chunksetin accordance with the round robin scheme.

Each DST execution unit of the associated DST execution unit memoriesexecutes a partial task on each slice in accordance with an executionordering. For example, a first job runs to execute partial tasks onslices of a first chunk and a second job runs to execute partial taskson slices of a second chunk. As another example, a first job runs on afirst slice of each chunk of each chunkset and a second job runs on asecond slice of each chunk of each chunkset.

FIG. 41C is a flowchart illustrating an example of assigning slices andpartial tasks to distributed storage and task (DST) execution units,which include similar steps to FIGS. 5 and 40C. The method begins withstep 126 of FIG. 5 where a processing module (e.g., of a DST clientmodule) receives data and the corresponding task and continues with step366 of FIG. 40C where the processing module selects one or more DSTexecution units for the task based on a capability level associated witheach of the DST execution units. The method continues with steps 130,132, 134, and 136 of FIG. 5 where the processing module determinesprocessing parameters of the data based on a number of DST executionunits, determines task partitioning based on the DST execution units andthe processing parameters, processes the data in accordance with theprocessing parameters to produce slice groupings, and partitions thetask based on the task partitioning to produce partial tasks.

The method continues at step 378 where the processing module determinespartial task execution ordering for each partial task. The determiningmay be based on one or more of a requirement (e.g., task executionlatency, task execution capability, storage reliability level), a datatype, a DST execution unit capability level, a chunk identifier, apillar number, and a slice name. The method continues at step 380 wherethe processing module determines a pillar mapping based on the partialtask execution ordering and a reliability requirement. The determiningof the pillar mapping produces an indication of which slice grouping isto be sent to which DST execution unit. The determining may be based onone or more of a pillar selection scheme (e.g., common chunk, roundrobin), a predetermination, a previous determination, a lookup, a query,and receiving the pillar mapping. The method continues at step 382 wherethe processing module sends the slice groupings and the correspondingpartial tasks to the DST execution units in accordance with the pillarmapping.

FIG. 42A is a diagram illustrating another example of mapping slicegroupings to a set of distributed storage and task (DST) execution unitmemories. The mapping includes a slice to memory mapping for a pillarwidth number of DST execution unit memories. For example, for DSTexecution unit memories 1-4 are utilized to store 3 pillars of slicesand 1 pillar of error coded slices when a pillar width is 4 and a decodethreshold number is 3.

Slices associated with one or more chunksets are distributed to the DSTexecution unit memories in accordance with a DST execution unitselection scheme. Slices of a common chunk number (e.g., a common pillarnumber) are sent to a common DST execution unit when the selectionscheme includes selecting a same DST execution unit for slicesassociated with a common pillar. For example, slices of each chunk 1 aresent to DST execution unit 1, slices of each chunk 2 are sent to DSTexecution unit 2, slices of each chunk 3 are sent to DST execution unit3, and error coded slices of each chunk 4 are sent to DST execution unit4.

For each chunk of each chunkset, associated partial tasks are sent to aDST execution unit that stores the chunk. For example, partial task 1-2(e.g., chunk 1, chunkset 2) is sent to DST execution unit 1 when slicesassociated with chunk 1 of chunkset 2 are stored at DST execution unit1.

One or more slices are selected in accordance with a redundancy schemeto produce one or more redundant slices. The redundancy scheme indicateshow selection is accomplished and may be based on one or more of areliability requirement, an access latency requirement, a DST executionunit performance level, and a DST execution unit reliability level. Forexample, the selecting includes selection of all slices. As anotherexample, the selecting includes at least one slice per chunk perchunkset. As yet another example, the selecting includes at least oneslice per chunkset. The one or more redundant slices are stored in a DSTexecution unit of the pillar width number of DST execution units inaccordance with a pillar mapping. For example, all of the redundantslices are stored in a DST execution unit associated with storage oferror coded slices when the pillar mapping includes storing theredundant slices in the DST execution unit associated with the storageof error coded slices.

A slice in error (e.g., missing, corrupted, a stored integrity valuedoes not match a calculated integrity value) may be remedied byrebuilding or retrieving. A slice in error may further be remedied byreplacing the slice and error with a corresponding redundant slice. Forexample, slice 2,1,2 is rebuilt by retrieving redundant slice 2,1,2 whenslice 2,1,2 is in error. A slice in error may be remedied by rebuildingby utilizing at least a decode threshold number of associated slices,wherein each associated slice is associated with a common segment of theslice in error slice. The associated slices includes slices, error codedslices, and redundant slices of the common segment. For example, slice3,2,1 is rebuilt from associated slices including slice 3,1,1, slice3,3,1, and error coded slice 3,4,1 when slice 3,2,1 is in error and aredundant slice corresponding to slice 3,2,1 is not available. Asanother example, slice 4,2,2 is rebuilt from associated slices includingslice 4,1,2, error coded slice 4,4,2, and redundant slice 4,3,2, whenslice 4,2,2 is in error and utilization of slice 4,3,2 from DSTexecution unit 3 is undesirable (e.g., DST execution unit 3 isunavailable, not trusted, or too busy).

FIG. 42B is a flowchart illustrating another example of assigning slicesand partial tasks to distributed storage and task (DST) execution units,which include similar steps to FIGS. 5 and 40C. The method begins withstep 126 of FIG. 5 where a processing module (e.g., of a DST clientmodule) receives data and the corresponding task and continues with step366 of FIG. 40C where the processing module selects one or more DSTexecution units for the task based on a capability level associated witheach of the DST execution units. The method continues with steps 130,132, 134, and 136 of FIG. 5 where the processing module determinesprocessing parameters of the data based on a number of DST executionunits, determines task partitioning based on the DST execution units andthe processing parameters, processes the data in accordance with theprocessing parameters to produce slice groupings, and partitions thetask based on the task partitioning to produce partial tasks.

The method continues at step 384 where the processing module selects oneor more slices of the slice groupings in accordance with a redundancyscheme to produce one or more redundant slices. The redundancy schemeindicates how selection is accomplished and may be based on one or moreof a reliability requirement, an access latency requirement, a DSTexecution unit performance level, and a DST execution unit reliabilitylevel. The method continues at step 386 where the processing moduledetermines pillar mapping for the slice groupings and the one or moreredundant slices based on a partial task execution requirement and astorage reliability requirement. For example, processing moduledetermines the pillar mapping to store the one or more redundant slicesin a DST execution unit associated with a favorable performance leveland a favorable available storage capacity level. The method continuesat step 388 where the processing module sends the slice groupings andcorresponding partial tasks to the DST execution units in accordancewith the pillar mapping. The sending may include outputting the slicegroupings and corresponding partial tasks in accordance with taskexecution ordering. The method continues at step 390 where theprocessing module sends the one or more redundant slices to at least oneDST execution unit in accordance with the pillar mapping.

FIG. 42C is a flowchart illustrating an example of retrieving a slicefor partial task processing. The method begins at step 392 where aprocessing module (e.g., of a distributed storage and task (DST) clientmodule of a DST execution unit) identifies a slice (e.g., produces aslice name) for processing based on a corresponding partial task. Theidentifying includes at least one of retrieving a partial task forexecution, extracting a slice name from the partial task, extracting achunk identifier (ID) from the partial task, and obtaining a slice nameof the slice based on the chunk ID (e.g., a table lookup). The methodcontinues at step 394 where the processing module determines whether theslice is available locally. The determining may be based on one or moreof issuing a local read slice request and receiving a local read sliceresponse, a local storage table lookup, and receiving a local slice namelist. The method branches to step 398 when the slice is not availablelocally. The method continues to step 396 when the slice is availablelocally. The method continues at step 396 where the processing moduleretrieves the slice locally. The method branches to step 406.

The method continues at step 398 where the processing module determineswhether a redundant slices available from another DST execution unitwhen the processing module determines that the slice is not availablelocally. The determining may be based on at least one of a retrievalattempt, a query, a redundant slice location table, and a slice locationextracted from the partial task. The determining may include obtaining aDST execution unit ID associated with the other DST execution unit. Themethod branches to step 402 when the redundant slice is not available.The method continues to step 400 when the redundant slice is available.The method continues at step 400 where the processing module retrievesthe redundant slice from the other DST execution unit. For example, theprocessing module generates a slice retrieval request that includes theslice name, sends the request to the other DST execution unit based onthe other DST execution unit ID, and receives the redundant slice. Themethod branches to step 404.

The method continues at step 402 where the processing module facilitatesrebuilding the slice to produce a rebuilt slice when the redundant sliceis not available. The facilitating includes at least one of utilizing arebuilding process and utilizing a zero-information gain (ZIG)rebuilding process. The processing module retrieves a decode thresholdnumber of slices corresponding to the slice (e.g., a common segment),decodes the decode threshold number of slices to reproduce a datasegment, re-encodes the data segment to produce a pillar width number ofslices that includes the rebuilt slice when the rebuilding process isutilized. The retrieving the decode threshold number of slicescorresponding to the slice includes generating at least a decodethreshold number of read slice requests, sending the at least the decodethreshold number of read slice requests to other DST execution units,and receiving the at least the decode threshold number of slicescorresponding to the slice.

The processing module retrieves a decode threshold number of ZIG partialslices and decodes (e.g., exclusive OR) the ZIG partial slices toreproduce the rebuilt slice when the ZIG rebuilding process is utilized.The retrieving the decode threshold number of ZIG partial slicesincludes generating at least a decode threshold number of ZIG partialslice requests, sending the at least the decode threshold number of ZIGpartial slice requests to the other DST execution units, wherein each ofthe other DST execution units generates a ZIG partial slice, andreceiving the decode threshold number of ZIG partial slices. Thegenerating of a ZIG partial slice by a DST execution unit of the otherDST execution units includes reducing a generator matrix to produce asquare matrix that exclusively includes rows identified in the partialrequest (e.g., slice pillars associated with participating units of adecode threshold number of units), invert the square matrix to producean inverted matrix (e.g., alternatively, may extract the inverted matrixfrom the request), matrix multiply the inverted matrix by a local slice(e.g., of same segment as slice to be rebuilt) to produce a vector, andmatrix multiply the vector by a row of the generator matrixcorresponding to the desired slice to be rebuilt (e.g., alternatively,may identify the row from the request), to produce the requested ZIGpartial slice.

The method continues at step 404 where the processing module stores oneof the rebuilt slice and the redundant slice locally. As such, a remedyis provided to the slice not being available locally. Subsequent accessof the redundant slice locally may avoid burdening a primary DSTexecution unit. The method continues at step 406 where the processingmodule processes one of the slice, the rebuilt slice, and the redundantslice in accordance with the corresponding partial task to produce apartial result. The processing may include outputting the partialresult.

FIG. 43A is a schematic block diagram of another embodiment of adistributed storage and task (DST) execution unit 408 that includes acontroller 410, a memory 414, and a distributed task (DT) executionmodule 412. The memory 414 is operational to provide a storage taskqueue 416, a slice memory 418, and a computing task queue 420. Thecontroller 410 is operational to receive slices 430, store the slices430 in the slice memory 418, receive slice access requests 422, storethe slice access requests 422 in the storage task queue 416, receivepartial task requests 424, store the partial tasks 424 in the computingtask queue 420, determine a prioritization for the slice access requests422 and the partial task requests 424, update prioritization of sliceaccess requests 422 in the storage task queue 416, update prioritizationof partial tasks 424 stored in the computing task queue 420, facilitateexecution of the slice access requests 422 and the partial task requests424 in accordance with the prioritization to produce slice accessresponses 426 and partial results 428, and output the slice accessresponses 426 and the partial results 428. Each slice access request 422of the slice access requests 422 includes at least one of a read requestand a write request. Each slice access response 426 of the slice accessresponses 426 includes at least one of a read responses and a writeresponse. The DT execution module 412 is operational to retrieve slices430 from slice memory 418, retrieve partial tasks 424 from the computingtask queue 420, and perform partial tasks 424 on the slices 430 inaccordance with prioritization of the partial tasks 424 to produce thepartial results 428.

In an example of operation, the controller 410 receives a slice accessrequest 422 that includes a read slice request for 100 slices availablefrom the slice memory 418. The controller 410 stores read slice requestin the storage task queue 416. Next, the controller 410 receives apartial task 424 that includes a computing task to search 10,000 slicesavailable from the slice memory 418 for a keyword to identify each slicethat includes the keyword. The controller 410 stores the partial task424 in the computing task queue 420.

At least one of the controller 410, the DT execution module 412, and aDST client module updates a computing task prioritization of entries inthe computing task queue 420 (e.g., including the entry to search the10,000 slices) based on one or more of task execution requirements, taskexecution performance level information, and task execution capabilitylevel information. The task execution requirements includes one or moreof a capacity threshold, a loading threshold, a computing task executionperformance level goal, a storage task execution performance level goal,a partial task priority level, and a storage task priority level. Thetask execution performance level information includes one or more of aDT execution module loading level, historic computing task executionperformance level information, and historic storage task executionperformance level information. The task execution capability levelinformation includes memory availability, available memory capacity, andavailable DT execution module processing capability. For example, thecontroller updates the computing task prioritization such that thepartial task to search the 10,000 slices is prioritized lower than aprevious task retrieved from the computing task queue to sort data of300 slices when the previous task associated with sorting is associatedwith a partial task priority level that is greater than a partial taskpriority level associated with the partial task to search the 10,000slices.

The at least one of the controller 410, the DT execution module 412, andthe DST client module updates a storage task prioritization of entriesin the storage task queue 416 (e.g., including the read request for the100 slices) based on one or more of the task execution requirements, thetask execution performance level information, and the task executioncapability level information. For example, the controller 410 updatesthe storage task prioritization such that the partial task to read the100 slices is prioritized higher than a previous task retrieved from thestorage task queue to write 200 slices when the previous task associatedwith writing is associated with a storage task priority level that islower than a storage task priority level associated with the storagetask to read the 100 slices.

In the example of operation continued, the at least one of thecontroller 410, the DT execution module 412, and the DST client modulefurther updates the computing task prioritization and the storage taskprioritization in accordance with a task prioritization scheme. The taskprioritization scheme includes prioritization between storage tasks andcomputing tasks. For example, the task prioritization scheme indicatesto prioritize storage tasks over computing tasks. As another example,the task prioritization scheme indicates to prioritize computing tasksover storage tasks. As yet another example, the task prioritizationscheme indicates to prioritize storage tasks and computing tasksindependently from each other. For instance, the task prioritizationscheme indicates to privatize storage tasks to maintain a storagecapacity utilization level below a storage capacity utilization levelthreshold and to prioritize computing tasks to maintain a computing taskcapacity utilization level below a computing task capacity utilizationlevel threshold. Alternatively, the at least one of the controller 410,the DT execution module 412, and the DST client module updates thestorage task prioritization and the computing task prioritization in onecycle based one or more of the task execution requirements, the taskexecution performance level information, the task execution capabilitylevel information, and the task prioritization scheme.

Next, the controller 410 executes storage tasks from the storage taskqueue 416 and the DT execution module 412 executes computing tasks fromthe computing task queue 420 in accordance with updated taskprioritization. For example, the DT execution module 412 retrieves thepartial task to search the 10,000 slices from the computing task queue420, initiates execution of the partial task in accordance with thecomputing task prioritization, which when activated, retrieves the10,000 slices from the slice memory 418, performs the search on the10,000 slices for the keyword to identify slices that include thekeyword, generates partial results 428 that includes the identificationof the identify slices, and outputs the partial results 428. As anotherexample, the controller 410 retrieves the read 100 slices request fromthe storage task queue 416, initiates execution of the read request inaccordance with the storage task prioritization, which when activated,retrieves the 100 slices from the slice memory 418, and outputs the 100slices.

FIG. 43B is a flowchart illustrating an example of prioritizing apartial task. The method begins at step 432 where a processing module(e.g., of a distributed storage and task (DST) execution unit) receivesa slice grouping and corresponding partial tasks. The method continuesat step 434 where the processing module stores the slice grouping in aslice memory and stores the corresponding partial tasks in a computingtask queue. The storing includes at least one of appending the slicegrouping to an end of previously stored slices in the slice memory,appending the corresponding partial tasks to an end of previously storedpartial tasks in the computing task queue, and initializing a computingtask prioritization. The initializing of the computing taskprioritization includes obtaining a prioritization level and updatingthe computing task queue to include the prioritization level. Theobtaining includes at least one of utilizing a default prioritizationlevel, retrieving a prioritization level, and receiving theprioritization level.

The method continues at step 436 where the processing module receives aplurality of slice access requests. Each slice access request of theplurality of slice access requests includes one or more of a requesttype indicator (e.g., read, write), a slice, a slice name, an accessrequirement, a priority level indicator, a data type indicator (e.g.,video, text, image, audio, etc.), and a requesting entity identifier(ID). The method continues at step 438 where processing module storesthe plurality of slice access requests in a storage task queue. Thestoring includes at least one of appending the slice access requests toan end of previously stored slice access requests and initializing astorage task prioritization. The initializing of the storage taskprioritization includes obtaining a prioritization level and updatingthe storage task queue to include the prioritization level. Theobtaining includes at least one of utilizing a default prioritizationlevel, retrieving a prioritization level, and receiving theprioritization level.

The method continues at step 440 where the processing module obtainstask execution requirements. The obtaining includes at least one ofinitiating a query, receiving, a lookup, and receiving with the sliceaccess requests. The method continues at step 442 where the processingmodule obtains task execution performance level information. Theobtaining includes at least one of initiating a performance test,initiating a query, receiving, a lookup, receiving with the slice accessrequests, and accessing historical performance level records. The methodcontinues at step 444 where the processing module obtains task executioncapability level information. The obtaining includes at least one ofinitiating an availability test, initiating a query, receiving, alookup, receiving with the slice access requests, accessingconfiguration information, and accessing historical capability levelrecords.

The method continues at step 446 where the processing module updatescomputing task prioritization of the computing task queue based on oneor more of the task execution requirements, the task executionperformance level information, and the task execution capability levelinformation. For example, the processing module raises priority forhigher priority tasks. As another example, the processing module lowerspriority for more tasks when performance is unfavorable. As yet anotherexample, the processing module raises priority for more tasks whenfavorable capability exists.

The method continues at step 448 where the processing module updatesstorage task prioritization of the storage task queue based on one ormore of the task execution requirements, the task execution performancelevel information, and the task execution capability level information.For example, the processing module raises priority for higher prioritytasks. As another example, the processing module lowers priority formore tasks when performance is unfavorable. As yet another example, theprocessing module raises priority for more tasks when favorablecapability exists.

The method continues at step 450 where the processing module obtains atask prioritization scheme for prioritizing one or more computing taskswith one or more storage tasks. The obtaining includes at least one ofanalyzing performance by type, comparing performance by type, initiatinga query, receiving the scheme, a lookup, utilizing a predeterminedscheme, and accessing configuration information. The method continues atstep 452 where the processing module updates the computing taskprioritization of the computing task queue based on the taskprioritization scheme. The method continues at step 454 where theprocessing module updates the storage task prioritization of the storagetask queue based on the task prioritization scheme. The method continuesat step 456 where the processing module executes tasks in accordancewith the storage task queue and the computing task queue.

FIG. 44A is a diagram illustrating another example of mapping slicegroupings to a set of distributed storage and task (DST) execution unitmemories. The mapping includes a slice to memory mapping for a pillarwidth number of DST execution unit memories. For example, for DSTexecution unit memories 1-5 are utilized to store 3 pillars of slicesand 2 pillars of error coded slices when a pillar width is 5 and adecode threshold number is 3.

Slices associated with one or more chunksets are distributed to the DSTexecution unit memories 1-5 in accordance with a DST execution unitselection scheme of a pillar mapping scheme. The pillar mapping schemeidentifies at least one of a number of DST execution units to utilizefor the storage of chunks and a number of DST execution units to utilizefor the storage of pillars of error coded slices. For example, thenumber of DST execution units to utilize for the storage of chunks ischosen to be a pillar width number when the pillar mapping schemeincludes maximizing a number of DST execution units to execute partialtasks on stored chunks.

The DST execution unit selection scheme includes one of a round robinapproach and a common pillar approach. When the DST execution unitselection scheme includes the common pillar approach, slices of a commonchunk number (e.g., a common pillar number) are sent to a common DSTexecution unit. For example, slices of each chunk 1 are sent to DSTexecution unit 1, slices of each chunk 2 are sent to DST execution unit2, slices of each chunk 3 are sent to DST execution unit 3, error codedslices of each chunk 4 are sent to one or more of the DST executionunits 1-5 (e.g., DST execution unit 4), and error coded slices of eachchunk 5 are sent to one or more of the DST execution units 1-5 (e.g.,DST execution unit 5).

When the DST execution unit selection scheme includes the round robinapproach, chunks from different chunksets and of a same chunk number aresent to a different DST execution unit memory from chunkset to a nextchunkset. For example, slices of a chunk 1 of a chunkset 1 are sent toDST execution unit 1, slices of a chunk 1 of a chunkset 2 are sent toDST execution unit 5, slices of a chunk 1 of a chunkset 3 are sent toDST execution unit 4, and slices of a chunk 1 of a chunkset 4 are sentto DST execution unit 4 etc. As another example, error coded slices of apillar 5 of chunkset 1 are sent to DST execution unit 5, error codedslices of a pillar 5 of chunkset 2 are sent to DST execution unit 4,error coded slices of a pillar 5 of chunkset 3 are sent to DST executionunit 3, and error coded slices of a pillar 5 of chunkset 4 are sent toDST execution unit 2.

For each chunk of each chunkset, associated partial tasks are sent to aDST execution unit that stores the chunk. For example, partial task 1-2(e.g., chunk 1, chunkset 2) is sent to DST execution unit 5 when slicesassociated with chunk 1 of chunkset 2 are stored at DST execution unit5. The round robin approach provides a system improvement by providing abalancing of partial task assignments across the pillar width number ofDST execution units 1-5. A further improvement is provided when evenmore chunks of further chunksets are distributed amongst the DSTexecution units 1-5.

FIG. 44B is a diagram illustrating another example of mapping slicegroupings to a set of distributed storage and task (DST) execution unitmemories. The mapping includes a slice to memory mapping for a decodethreshold number of DST execution unit memories. For example, for DSTexecution unit memories 1-3 are utilized to store 3 pillars of slicesand 2 pillars of error coded slices when a pillar width is 5 and adecode threshold number is 3.

Slices associated with one or more chunksets are distributed to the DSTexecution unit memories 1-3 in accordance with a DST execution unitselection scheme of a pillar mapping scheme. The pillar mapping schemeidentifies at least one of a number of DST execution units to utilizefor the storage of chunks and a number of DST execution units to utilizefor the storage of pillars of error coded slices. For example, thenumber of DST execution units to utilize for the storage of chunks ischosen to be the decode threshold number when the pillar mapping schemeincludes limiting a number of DST execution units to execute partialtasks on stored chunks to the decode threshold number.

The DST execution unit selection scheme includes one of a round robinapproach and a common pillar approach. When the DST execution unitselection scheme includes the common pillar approach, slices of a commonchunk number (e.g., a common pillar number) are sent to a common DSTexecution unit. For example, slices of each chunk 1 are sent to DSTexecution unit 1, slices of each chunk 2 are sent to DST execution unit2, and slices of each chunk 3 are sent to DST execution unit 3.

When the DST execution unit selection scheme includes the round robinapproach, one or more of slices from different chunksets and error codedslices of error coded pillars are sent to a different DST execution unitmemory from chunkset to a next chunkset. For example, slices of a chunk1 of a chunkset 1 are sent to DST execution unit 1, slices of a chunk 1of a chunkset 2 are sent to DST execution unit 3, slices of a chunk 1 ofa chunkset 3 are sent to DST execution unit 2, and slices of a chunk 1of a chunkset 4 are sent to DST execution unit 1 etc. As anotherexample, error coded slices of a pillar 5 of chunkset 1 are sent to DSTexecution unit 2, error coded slices of a pillar 5 of chunkset 2 aresent to DST execution unit 1, error coded slices of a pillar 5 ofchunkset 3 are sent to DST execution unit 3, and error coded slices of apillar 5 of chunkset 4 are sent to DST execution unit 2. For each chunkof each chunkset, associated partial tasks are sent to a DST executionunit that stores the chunk. For example, partial task 1-2 (e.g., chunk1, chunkset 2) is sent to DST execution unit 1 when slices associatedwith chunk 1 of chunkset 2 are stored at DST execution unit 1.

FIG. 44C is a flowchart illustrating another example of assigning slicesand partial tasks to distributed storage and task (DST) execution units,which include similar steps to FIGS. 5 . The method begins with step 126of FIG. 5 where a processing module (e.g., of a DST client module)receives data and the corresponding task and continues at step 458 wherethe processing module obtains a pillar mapping scheme. The pillarmapping scheme includes at least one of a common pillar approach and around robin approach for one or more of slices of chunks and error codedslices. The obtaining includes at least one of determining based on thedata, a query, a lookup, and receiving the pillar mapping scheme.

The method continues step 460 where the processing module determines anumber of DST execution units for task execution. The determining may bebased on one or more of the pillar mapping scheme, the data, the task,and an execution requirement. For example, processing module determinesthe number of DST execution units for task execution to be five when anexecution requirement requires five DST execution units to execute thepartial tasks within a required timeframe. The method continues at step462 where the processing module determines a number of DST executionunits for storage of slice groupings. The determining may be based onone or more of the number of DST execution units for task execution, thepillar mapping scheme, the data, the task, and a storage requirement.For example, the processing module determines a number of DST executionunits for storage of slice groupings to be three when error coded slicesare required to be stored in at least three DST execution units.

The method continues at step 464 where the processing module selects DSTexecution units in accordance with the number of DST execution units fortask execution and the number of DST execution units for storage ofslice groupings. The selecting may also be based on a performancerequirement, reliability requirement, a capacity requirement, DSTexecution unit reliability history, a DST execution unit capacity level,and DST execution unit performance history. The method continues at step466 where the processing module determines processing parameters of thedata based on the number of DST execution units for task execution, thenumber of DST execution units for storage of slice groupings, and thepillar mapping scheme. For example, a decode threshold number isestablished to be substantially five which is the same as the number ofDST execution units for task execution when the pillar mapping schemeincludes minimizing the number of DST execution units. As anotherexample, a decode threshold number is established as 10 based onreliability requirement when the pillar mapping scheme indicates toutilize a maximum number of DST execution units. As yet another example,a pillar width number is established to be the same as the number of DSTexecution units for storage of slice groupings when the pillar mappingscheme is to maximize the number of DST execution units.

The method continues at step 468 where the processing module determinestask partitioning based on the number of DST execution units for taskexecution and the pillar mapping scheme. For example, the taskpartitioning is established to be a round robin approach when the pillarmapping scheme includes maximizing the number of units as a pillar widthnumber of units. As another example, the task partitioning establishedto align common pillars of each chunkset with a common DST executionunit as partial tasks are evenly distributed amongst the DST executionunits when the pillar mapping scheme includes minimizing the number ofDST execution units (e.g., when a number of DST execution units for taskexecution is substantially the same as the decode threshold number).

The method continues with steps 134-136 of FIG. 5 where the processingmodule processes the data in accordance with the processing parametersto produce slice groupings and partitions the task based on the taskpartitioning to produce partial tasks. The method continues at step 470where the processing module sends the slice groupings and correspondingpartial tasks to the selected DST execution units in accordance with thepillar mapping scheme. As such, slices of the slice groupings are storedin one or more of the selected DST execution units for subsequentpartial task execution and error coded slices of the slice groupings arestored in one or more of the selected DST execution units for storage.

FIG. 45A is a schematic block diagram of another embodiment of adistributed storage and task (DST) execution unit 472 that includes acontroller 474, a memory 478, and a distributed task (DT) executionmodule 476. The memory 478 is operational to provide a slice memory 482and a computing task queue 480. The controller 474 is operational toreceive slices 488, store the slices 488 in the slice memory 482,receive slice access requests 483, receive index slice access requests483, receive partial task requests 484, store the partial tasks 484 inthe computing task queue 480, facilitate execution of the slice accessrequests 483, the index slice access requests 483, and the partial taskrequests 484 to produce slice access responses (e/g/. slices 488), indexslice access responses (e.g., index slices 486), zero information gain(ZIG) partial index slices 490, partial results 492, and output theslice access responses, the index slice access responses, the ZIGpartial index slices 490, and the partial results 492. Each slice accessrequest 483 of the slice access requests 483 includes at least one of aread request and a write request. Each slice access response of theslice access responses includes at least one of a read responses and awrite response. The DT execution module 476 is operational to retrieveslices 488 from the slice memory 482, retrieve partial tasks 484 fromthe computing task queue 480, retrieve index slices 486 from the slicememory 482, and perform partial tasks 484 on the slices 488 and/or theindex slices 486 to produce updated index slices 494, the ZIG partialindex slices 490, and the partial results 492.

In an example of operation, the DT execution module 476 retrieves slices488 of a corresponding chunk from the slice memory 482 and retrievespartial tasks 484 associated with the chunk from the computing taskqueue 480. When the partial tasks 484 include an indexing partial task,the DT execution module 476 processes the chunk in accordance with theindexing partial tasks associated with the chunk to produce indexinformation. The index information includes an indexing partial result.Next, the DT execution module 476 retrieves a corresponding index slice486 from the slice memory 482 and updates the index slice utilizing theindex information to produce the updated index slice 494 when thecorresponding index slice is available (e.g., as a first time null indexslice or as a previously stored index slice). The DT execution module476 stores the updated index slice 494 in the slice memory 482 such thatthe controller 474 can subsequently retrieve the index slice 486 inresponse to an index slice access request 483. Next, for each errorcoded pillar associated with the index slice 486, the DT executionmodule 476 generates error coded slice modification information based onone or more of the updated index slice 494, the index slice 486, and ZIGpartial slice generation information. The DT execution module 476 sendsthe error coded slice publication information to one or more other DSTexecution units where each of the one or more other DST execution unitsgenerates and stores an updated error coded index slice. The updatederror coded index slice may be utilized for subsequent rebuilding of theindex slice 486.

The ZIG partial slice generation information includes one or more of agenerator matrix, an inverted square matrix, a decode threshold numberof participating pillar numbers, and a pillar number associated with thepillar. The generating of the error coded slice modification informationincludes generating the error coded slice modification information inaccordance with an expression: error coded slice modificationinformation=(ZIG partial updated index slice) XOR (ZIG partial indexslice), wherein XOR is an exclusive OR function. A ZIG partial slice isgenerated by reducing the generator matrix to produce a square matrixthat exclusively includes rows identified in the ZIG partial slicegeneration information (e.g., participating pillar numbers), invert thesquare matrix to produce the inverted score matrix (e.g., alternatively,may extract the inverted square matrix from the ZIG partial slicegeneration information), matrix multiply the inverted square matrix bythe slice (e.g., updated index slice, index slice) to produce a vector,and matrix multiply the vector by a row of the generator matrixcorresponding to the error coded slice to be partial encoded (e.g.,alternatively, may extract the row corresponding to the pillar numberassociated with the pillar of the ZIG partial slice generationinformation) to produce the ZIG partial (updated) index slice 490.

FIG. 45B is a flowchart illustrating an example of generating an index.The method begins at step 496 where a processing module (e.g., of adispersed storage and task (DST) execution unit) retrieves an indexingpartial task for a slice (e.g., from a slice memory). The retrieving mayalso include one or more of identifying the indexing partial task basedon receiving the slice, ingesting data to produce the slice, andidentifying the partial task based on the ingested data. The methodcontinues at step 498 where the processing module obtains the slice forindexing. The obtaining includes one or more of obtaining a slice name(e.g., retrieving, generating, extracting from a partial task),receiving the slice, retrieving the slice from a slice memory,requesting the slice, and identifying the slice based on a pendingindexing partial task.

The method continues at step 500 where the processing module generatesindex information for the slice in accordance with the indexing partialtask. The generating includes processing the slice in accordance withthe indexing partial task to produce a partial result that includes theindex information. The method continues at step 502 where the processingmodule generates an updated index slice utilizing the index information.The generating includes retrieving a corresponding index slice from aslice memory and modifying the index slice based on the indexinformation to produce the updated index slice. The index slice mayinclude a null slice when no previous update process has been executed.The index slice may include a result of a previous update index sliceprocess that resulted in the index slice been stored in the slicememory.

The method continues at step 504 where the processing module facilitatesstorage of the updated index slice. The facilitating includes at leastone of storing the updated index slice in the slice memory, replacingthe index slice with the updated index slice in the slice memory, andsending the updated index slice to another DST execution unit forstorage therein. The method continues at step 506 where the processingmodule generates error coded slice modification information based on theupdated index slice as discussed with reference to FIG. 45A. The methodcontinues at step 508 where the processing module outputs the errorcoded slice modification information to one or more other DST executionunits. The outputting includes identifying the one or more other DSTexecution units as DST execution units utilize to store error codedslices associated with the slice. For example, the processing moduleidentifies DST execution units 4 and 5 when DST execution units 1-3 areutilized to store chunks (e.g., of index slices) and DST execution units4 and 5 are utilized to store error coded index slices associated withthe chunks of index slices.

FIG. 46 is a flowchart illustrating an example of identifying a portionof a slice groupings. The method begins at step 510 where a processingmodule (e.g., of a dispersed storage and task (DST) execution module)obtains a partial task for slice grouping. The partial task includes apartial task associated with an index utilized to locate and/or identifydata stored as one of more slice groupings in a DST module. The partialtask includes at least one of a computation task, an index typeindicator, an index search term, a slice name, and a slice groupingidentifier (ID). The obtaining includes at least one of identifying anext partial task, retrieving the partial task from a local memory, andreceiving the partial task, wherein the partial task is associated withthe slice grouping.

The method continues at step 512 where the processing module selects atleast one index of a set of indexes based on the partial task. The setof indexes may be utilized to locate and/or identify data utilizing aset of index types and associated set of index search terms. Theselecting includes at least one of identifying the at least one index asan index associated with an index type that substantially matches andindex type of the partial task.

The method continues at step 514 where the processing module identifiesone or more portions of the slice grouping based on the selected atleast one index and the partial task. The identifying includesextracting and index search term from the partial task and utilizing thesearch term to search the index to identify the one or more portions ofthe slice grouping. The method continues at step 516 where theprocessing module processes the identifying one or more portions of theslice grouping utilizing the partial task to produce partial results.The processing includes executing a computation of task of the partialtask on the identified one or more portions of the slice groupings toproduce partial results. The method continues at step 518 where theprocessing module outputs the partial results.

FIG. 47A is a diagram illustrating another example of mapping slicegroupings to a set of distributed storage and task (DST) execution unit(storage unit) memories. The mapping includes a record to memory mappingfor two or more DST execution unit memories. Each record includes anumber of bytes of data, wherein the data includes at least one of adata file, and a portion of a data file, and the number of bytes of thedata corresponds to the record. For example, an audio sample recordincludes 1000 acoustic sampling bytes. As another example, a text recordincludes a text document file. A chunk includes one or more slices of aslice grouping. For example, a chunk includes three slices. Each sliceof the one more slices includes at least a portion of a record. Forexample, slice 1 of chunk 1 includes record 1 and record 2. As anotherexample, slice 2 of chunk 1 includes record 3 and a portion of record 4.A plurality of chunks includes a plurality of slices, wherein two ormore chunks may include two or more slices corresponding to contiguousdata. For example, contiguous data may extend from a last slice of afirst chunk to a first slice of a second chunk. As such, a record may bemapped to two or more chunks. For example, a first portion of record 5is mapped to a portion of slice 3 of chunk 1 and a second portion ofrecord 5 is mapped to a portion of a slice 1 of a chunk 2.

Two or more DST execution unit memories may be assigned to a commonsite. As such, a record mapped to the two or more DST execution unitmemories at the common site may be readily retrieved for processing byany DST execution unit associated with the DST execution unit memories.Retrieving may include reading a slice to immediately execute a partialtask and pre-reading the slice to execute the partial task within a timeperiod (e.g., shortly thereafter without delay between execution of theprevious slice and execution of the slice). For example, DST executionunits 1 and 2 may readily retrieve both portions of record 5. A recordmay be mapped to two or more DST execution unit memories, wherein eachof the two or more DST execution unit memories are at different sites.For example, a record 8 part 1 is mapped to DST execution unit 2 memoryat site 1 and record 8 part 2 is mapped to DST execution unit 3 summaryat a site 2. As such, the record mapped to two or more DST executionunit memories located at two or more sites may not be readily retrievedfor processing except by a DST execution unit associated with a portionof the record mapped to a common site. For example, DST execution unit 2at site 1 can readily retrieve record 8 part 1 but not record 8 part 2at another site and DST execution unit 3 at site 2 can readily retrieverecord 8 part 2 but not record 8 part 1 at another site.

A read ahead process may facilitate determining when to read ahead anddetermining which slices to read ahead based on at least one of apartial task execution performance level and the mapping of slicegroupings to the set of DST execution unit memories such that there issubstantially no delay between execution of a partial task on a recordof a previous slice and execution of the partial task on another portionof the record from a next slice. For example, the read ahead processfacilitates execution of a partial task on the first portion of record 5and the second portion of record 5 without delay to include continuouscomputation.

In a read ahead process example of operation, slice 2 is retrieved fromDST execution unit 1 memory, wherein slice 2 includes a record 3 and afirst portion of a record 4. Execution of a partial task is initiated onrecord 3 and a pre-read of a second portion of record 4 is initiated,since the second portion of record 4 is available from a common DSTexecution unit memory, by retrieving slice 3 from the DST execution unit1 memory.

In another read ahead process example of operation, slice 3 is retrievedfrom DST execution unit 1 memory at site 1, wherein slice 3 includes thesecond portion of record 4 and the first portion of record 5. Executionof a partial task is initiated on record 5. A determination is madewhether to pre-read the second portion of record 5 based on the mapping.A pre-read of the second portion of record 5 is initiated, since thesecond portion of record 5 is available from DST execution unit memoryof a common site with the first portion, by retrieving the secondportion of record 5 from the DST execution unit 2 memory at site 1.

In yet another read ahead process example of operation, DST executionunit 2 retrieves slice 3 from DST execution unit 2 memory at site 2,wherein slice 3 includes the first portion of record 8. The DSTexecution unit 2 initiates execution of a partial task on the firstportion of record 8. A determination is made whether to pre-read thesecond portion of record 8 based on the mapping. DST execution unit 2determines not to pre-read the second portion of record 8 when thesecond portion of record 8 is not readily available (e.g., stored atanother site). As such, the execution of the partial task on the secondportion of record 8 is left to DST execution unit 3.

A DST execution unit may retrieve a slice from a DST execution unitmemory associated with the DST execution unit and determine to execute apartial task on a portion of the slice when the portion of the slice isassociated with a subsequent portion of a record, wherein the recordincludes a previous portion that is stored in another DST execution unitmemory at another site. For example, DST execution unit 3 retrievesslice 1 from the DST execution unit 3 memory. The DST execution unit 3determines whether to execute a partial task on any portion of slice 1.The DST execution unit 3 determines to execute a partial task on thesecond portion of record 8 when the second portion of record 8 is notreadily available to DST execution unit 2 associated with storing thefirst portion of record 8.

A DST execution unit may retrieve a slice from a DST execution unitmemory associated with the DST execution unit and determine not toexecute a partial task on a portion of the slice when the portion of theslice is associated with a subsequent portion of a record, wherein therecord includes a previous portion that is stored in another DSTexecution unit memory at another site. For example, DST execution unit 2retrieves slice 1 from the DST execution unit 2 memory. The DSTexecution unit 2 determines whether to execute a partial task on anyportion of slice 1. The DST execution unit 2 determines not to execute apartial task on the second portion of record 5 when the second portionof record 5 is readily available to DST execution unit 1 associated withstoring the first portion of record 5. The DST execution unit 2determines to execute a partial task on a first portion of record 6 fromslice 1.

FIG. 47B is a flowchart illustrating an example of retrieving slices.The method begins at step 520 where a processing module (e.g., of adispersed task (DT) execution module (storage unit)) retrieves a sliceof a chunk for execution of a partial task. The slice may include a nextslice for execution of the partial task. The method continues at step522 where the processing module identifies a record configuration of theslice (e.g., a mapping of the slice to at least one record). Theidentifying includes retrieving a mapping record, receiving the mappingrecord, and extracting mapping from the slice (e.g., searching for arecord identifier). The method continues at step 524 where theprocessing module facilitates processing of a partial task on at leastone record of the slice. The facilitating includes one or more ofretrieving the partial task associated with the slice, queuing the slicefor processing in accordance with the record configuration of the slice,or immediately executing the partial task.

The method continues at step 526 where the processing module determineswhether the slice includes a partial record based on the recordconfiguration of the slice. The method loops back to step 520 where theprocessing module retrieves a slice of a chunk for partial taskexecution to retrieve a next slice when the processing module determinesthat the slice does not include a partial record. The method continuesto step 528 when the processing module determines that the slice doesinclude a partial record.

The method continues at step 528 where the processing module identifiesa slice location of another slice that includes a remaining partialrecord corresponding to the partial record. The slice location includesat least one of a next slice of the chunk when the slice is not a lastslice of the chunk, a different chunk when the slice is the last sliceof the chunk, another DST execution unit (storage unit) memory when achunk map indicates that the chunk is assigned to another DST executionunit memory, or another site when the chunk map indicates that the chunkis assigned to a DST execution unit memory at the other site.

The method continues step 530 where the processing module determineswhether the slice location is favorable. The determining may be based onone or more of the slice location, network performance, apredetermination, an estimated amount of time to retrieve the slice, oran estimated amount of time until processing may begin on the slice. Forexample, the processing module indicates that the slice location isfavorable when the slices are at a same site. As another example, theprocessing module indicates that the slice location is favorable whenthe slice is at another site and there is enough time to retrieve theslice before processing of an associated partial task should begin. Themethod loops back to step 520 when the processing module determines thatthe slice location is unfavorable. The method continues to step 532 whenthe processing module determines that the slice location is favorable.

The method continues at step 532 where the processing module retrievesthe other slice from the slice location (e.g., another slice of thechunk, another slice of another chunk from a common DST execution unitmemory, another slice of another chunk from another DST execution unitmemory, or a common site). The method continues at step 534 where theprocessing module facilitates processing of the partial task on at leastone record of the other slice. The facilitating includes at least one ofqueuing the slice for processing after other records of the slice inaccordance with the record configuration of the slice and immediatelyprocessing the partial task on the at least one record.

FIG. 48A is a schematic block diagram of an encoder system. The encodersystem includes one or more of a dispersed storage (DS) error encodingfunction 536, a random key generator 538, a set of dispersed storage andtask execution units 1-4, a set of chunk 1-3 encryptors corresponding toDST execution units storing chunks, and a set of chunk 1-3 keygenerators corresponding to the set of chunk 1-3 encryptors. The encodersystem is operable to encrypt chunks of a data chunkset 540 for storageas encrypted chunk slices in DST execution units associated with storingchunks of the set of DST execution units. The data chunkset 540 mayinclude data for storage and additional authenticated data partitionedinto a decode threshold number of chunks. The additional authenticateddata may include one or more of a user identifier (ID), a nonce, a dataversion, a sequence number, a transaction number, a snapshot ID, afilename, a data ID, a timestamp, authentication information, acredential, and a vault ID.

The random key generator 538 is operable to generate a master key 542 byat least one of transforming a random number and retrieving a key. TheDS error encoding 536 is operable to encode the data chunkset 540utilizing a dispersed storage error coding function to produce a decodethreshold number of chunks and a pillar with number minus the decodethreshold number of corresponding error coded slices. For example, theDS error encoding 536 encodes the data chunkset 540 to produce threechunks and a fourth pillar of error coded slices. Each chunk includesone or more slices based on an amount of data of the data chunkset and anumber of bytes per slice. For example, each chunk includes a number ofbytes in accordance with an expression of number of chunk bytes=numberof data chunkset sites divided by the decode threshold number. As such,the DS error encoding 536 encodes the data chunkset 540 to include chunk1 slices, chunk 2 slices, chunk 3 slices, and pillar 4 error codedslices when the decode threshold is 3 and the pillar width is 4.

The DS error encoding 536 is further operable to generate slice namescorresponding to each slice of each chunk and slice names correspondingto each error coded slice of the error coded slices in accordance with avault identifier (ID) associated with the data chunkset and the pillarwidth number. For example, the DS error encoding 536 generates chunk 1slice names corresponding to the chunk 1 slices, chunk 2 slice namescorresponding to the chunk 2 slices, chunk 3 slice names correspondingto the chunk 3 slices, and slice names for the pillar 4 error codedslices.

Each chunk key generator of the set of chunk key generators is operableto generate a key set for each corresponding chunk, where the key setincludes one or more keys corresponding to each slice of thecorresponding chunk. For example, the chunk key generator may generate acommon key as the key set. As another example, the chunk key generatormay generate a unique key for each slice of the corresponding chunk. Thegenerating includes transforming the master key 542 and a portion ofcorresponding chunk slice names utilizing a deterministic function toproduce the key set. The portion of the corresponding chunk slice namesincludes at least one of a pillar number, a vault ID, a segment number,a block number, an object number, generation number, and a slice index.For example, the chunk key generator applies an exclusive OR (XOR)function on the master key 542 and the pillar number to produce aninterim result and applies a mask generating function to the interimresult to produce a common key as the key set. As another example, foreach slice of the chunk, the chunk key generator applies the XORfunction on the master key 542 and the segment number to produce aninterim result and applies a mask generating function to the interimresult to produce a corresponding key of the key set corresponding to aslice of the slices of the chunk.

Each chunk encryptor of the chunk encryptors is operable to encrypt eachslice of the corresponding chunk utilizing a corresponding key of acorresponding key set to produce encrypted chunk slices. For example,chunk 1 encryptor encrypts a first slice of the chunk 1 slices utilizinga first key of key set 1 to produce a first slice of encrypted chunk 1slices. As another example, chunk 3 encryptor encrypts a second slice ofthe chunk 3 slices utilizing a second key of key set 3 to produce asecond slice of encrypted chunk 3 slices. The encrypted chunk slices,the chunk slice names, the master key, the error coded slices, and theslice names for the error coded slices are sent to the set of DSTexecution units for storage therein. For example, encrypted chunk 1slices, chunk 1 slice names, and the master key is sent to DST executionunit 1 for storage therein. As another example, the pillar 4 error codedslices and the slice names for the pillar 4 error coded slices are sentto DST execution unit 4 for storage therein. Alternatively, or inaddition to, the master key 542 is encoded utilizing the dispersedstorage error coding function to produce a set of encoded master keyslices and the set of encoded master key slices are sent to the set ofDST execution units for storage therein.

FIG. 48B is a schematic block diagram of a dispersed storage system thatincludes a computing device 550 and a dispersed storage network (DSN)memory 552. The DSN memory 552 may be implemented utilizing one or moreof a distributed storage and task network (DSTN), a DSTN module, aplurality of storage nodes, one or more dispersed storage (DS) unitsets, and a plurality of dispersed storage (DS) units 554. Each DS unit554 may be implemented utilizing at least one of a storage server, astorage unit, a storage module, a memory device, a memory, a distributedstorage and task (DST) execution unit, a user device, a DST processingunit, and a DST processing module. The computing device 550 may beimplemented utilizing at least one of a server, a storage unit, a DSTNmanaging unit, a DSN managing unit, a DS unit 554, a storage server, astorage module, a DS processing unit, a DST execution unit, a userdevice, a DST processing unit, and a DST processing module. For example,computing device 550 is implemented as the DST processing unit. Thecomputing device 550 includes a dispersed storage (DS) module 556. TheDS module 556 includes a slice module 558, an encrypt module 560, and anoutput module 562.

The system functions to encode data 564 to produce slices 566, encryptthe slices 566 to produce encrypted slices 570, and store the encryptedslices 570 in the DSN memory 552. With regards to encoding the data 564,the slice module 558 performs a series of slicing steps. In a firstslicing step, the slice module 558 divides the data 564 into a pluralityof data segments. The slice module 558 divides the data 564 inaccordance with a segmentation scheme such that encoded data slices of acommon pillar of adjacent data segments include contiguous data portionof the data 564. For a data segment of the plurality of data segments,in a second slicing step, the slice module 558 encodes the data segmentusing a dispersed storage error encoding function to produce the set ofencoded data slices 566. Each slice may be associated with a slicegrouping of encoded data slices of a common pillar of other datasegments and includes one or more encoded data slices of a data chunk.The encoding includes arranging an encoding matrix and encoding toproduce encoded data slices of contiguous bytes of the data portion. Ina third slicing step, the slice module 558 generates slice names 568 foreach encoded data slice of the set of encoded data slices 566 to producea plurality of slice names 568, where a slice name 568 of the pluralityof slice names 568 includes a data identifier, a data segmentidentifier, and an encoded slice identifier. The slice name 568 mayfurther include at least one of identity of a target storage node (e.g.,DS unit 554) of the DSN memory 552, a security identifier, a randomnumber, a revision level number, and a transaction number.

With regards to encrypting the slices 566 to produce encrypted slices570, the encrypt module 560 is operable to select a subset of encodeddata slices (e.g., a decode threshold number) as a first type of encodeddata slices of the set of encoded data slices 566, where the datasegment was encoded utilizing a dispersed storage error encoding matrixthat includes a unity matrix section. The set of encoded data slices 566includes the first type of encoded data slices and a second type ofencoded data slices, where the first type of encoded data slicescorresponds to the unity matrix section and the second type of encodeddata slices corresponds to another section of the dispersed storageerror encoding matrix. Alternatively, the encrypt module 560 selects thesubset of encoded data slices as a third type of encoded data slices ofthe set of encoded data slices 566, where the set of encoded data slices566 includes the third type of encoded data slices and a fourth type ofencoded data slices, where the third type of encoded data slicesincludes encoded data slices based on data blocks and the fourth type ofencoded data slices includes encoded data slices based on data blocksand auxiliary blocks.

The encrypt module 560 may determine to encrypt the subset of encodeddata slices based on one or more of a predetermination, a request, aquery result, a sensitivity level of the data, and a vulnerability levelof the DSN memory 552. When the subset of encoded data slices of the setof encoded data slices 566 is to be encrypted, the encrypt module 560performs a series of encryption steps. In a first encryption step, theencrypt module 560 generates a master key. The encrypt module 560generates the master key based on one or more of a random number, anidentifier of the data chunk slice grouping, a lookup, and a private keyof a public-private key pair. In a second encryption step, the encryptmodule 560 selects a portion of the slice names 568 for the subset ofencoded data slices to produce a subset of selected slice name portions.The encrypt module 560 selects the portion of the slice names based onone or more of a predetermination, a request, a query result, thesensitivity level of the data, a required encryption level, and thevulnerability level of the DSN memory 552. In a third encryption step,the encrypt module 560 generates a subset of encryption keys based onthe master key and the subset of selected slice name portions. Theencrypt module 560 generates the subset of encryption keys by applying adeterministic function to the master key and the subset of selectedslice name portions. The deterministic function includes one or more ofa hashing function, a mask generating function, a hash-based messageauthentication code, and a sponge function. The encrypt module 560generates at least one key for slices of the data chunk and as many asone key per encoded data slice.

The encrypt module 560 generates an encryption key based on the masterkey and a data identifier as the subset of encryption keys for each ofthe plurality of data segments when the encrypt module 560 selects thedata identifier as the portion of the slice names. The encrypt module560 generates the encryption key based on the master key and the datasegment identifier as the subset of encryption keys for the data segmentwhen the encrypt module 560 selects the data segment identifier as theportion of the slice names. The encrypt module 560 generates the subsetof encryption keys based on the master key and each encoded data sliceidentifier of the subset of encoded data slices when the encrypt module560 selects the encoded slice identifier as the portion of the slicenames. The encrypt module 560 generates the subset of encryption keysbased on the master key and each pillar number of the subset of encodeddata slices when the encrypt module 560 selects a pillar number as theportion of the slice names, where the slice name further includes thepillar number. In a fourth encryption step, the encrypt module 560encrypts the subset of encoded data slices using the subset ofencryption keys to produce a subset of encrypted encoded data slices570. The encrypt module 560 may encrypt the entire data chunk slicegrouping at once or encrypt each encoded data slice one at a time usinga common encryption key for all slices or a different key for eachencoded data slice.

With regards to storing the encrypted slices 570 in the DSN memory 552,the output module 562 performs a series of output steps. In a firstoutput step, the output module 562 outputs the subset of encryptedencoded data slices 570 to the DSN memory 552 for storage therein. Theoutputting may include the output module 562 identifying DS units (e.g.,storage units) 554 of the DSN memory 552 requiring a higher securitythan other storage units 554 of the DSN memory 552 (e.g., more publiclyaccessible, less hacker protection, etc.) and selecting the subset ofencoded data slices as being targeted for storage in the storage unitsrequiring higher security. In a second output step, the output module562 outputs remaining encoded data slices 572 of the set of encoded dataslices 566 to the DSN memory 552 for storage therein.

FIG. 48C is a flowchart illustrating an example of encrypting slices.The method begins at step 580 where a processing module (e.g., of adistributed storage and task processing module) divides data into aplurality of data segments (e.g., in accordance with a data segmentationscheme such that encoded data slices of a common pillar of adjacent datasegments include contiguous data. For a data segment of the plurality ofdata segments, the method continues at step 582 where the processingmodule encodes the data segment using a dispersed storage error encodingfunction to produce a set of encoded data slices. The method continuesat step 584 where the processing module generates slice names for eachencoded data slice of the set of encoded data slices to produce aplurality of slice names, where a slice name of the plurality of slicenames includes a data identifier, a data segment identifier, and anencoded slice identifier. The slice name may further include at leastone of identity of a target storage node of the DSN, a securityidentifier, a random number, a revision level number, and a transactionnumber.

When a subset (e.g., a decode threshold number) of encoded data slicesof the set of encoded data slices is to be encrypted, the methodcontinues at step 586 where the processing module generates a masterkey. The generating may be based on one or more of a random number, anidentifier of the data chunk slice grouping, a lookup, and a private keyof a public-private key pair. The processing module may select thesubset of encoded data slices as a first type of encoded data slices ofthe set of encoded data slices, where the data segment was encodedutilizing a dispersed storage error encoding matrix that includes aunity matrix section. The set of encoded data slices includes the firsttype of encoded data slices and a second type of encoded data slices,where the first type of encoded data slices corresponds to the unitymatrix section and the second type of encoded data slices corresponds toanother section of the dispersed storage error encoding matrix.Alternatively, the processing module may select the subset of encodeddata slices as a third type of encoded data slices of the set of encodeddata slices, where the set of encoded data slices includes the thirdtype of encoded data slices and a fourth type of encoded data slices.The third type of encoded data slices includes encoded data slices basedon data blocks and the fourth type of encoded data slices includesencoded data slices based on data blocks and auxiliary blocks.

The method continues at step 588 where the processing module selects aportion of the slice names for the subset of encoded data slices toproduce a subset of selected slice name portions. The processing modulemay select one of the data identifier, the data segment identifier, theencoded slice identifier, and a pillar number as the portion of theslice names. The method continues at step 590 where the processingmodule generates a subset of encryption keys based on the master key andthe subset of selected slice name portions. The processing module maygenerate the subset of encryption keys by performing a deterministicfunction on the master key and the subset of selected slice nameportions. For example, the processing module performs a modulo additionof the master key and the subset of selected slice name portions toproduce the subset of encryption keys.

The processing module generates an encryption key based on the masterkey and the data identifier as the subset of encryption keys for each ofthe plurality of data segments when the processing module selects thedata identifier as the portion of the slice names. The processing modulegenerates the encryption key based on the master key and the datasegment identifier as the subset of encryption keys for the data segmentwhen the processing module selects the data segment identifier as theportion of the slice names. The processing module generates the subsetof encryption keys based on the master key and each of the encoded dataslice identifiers of the subset of encoded data slices when theprocessing module selects the encoded slice identifier as the portion ofslice names. The processing module generates the subset of encryptionkeys based on the master key and each of the pillar numbers of thesubset of encoded data slices when the processing module selects thepillar number as the portion of the slice names, where the slice namefurther includes the pillar number.

The method continues at step 592 where the processing module encryptsthe subset of encoded data slices using the subset of encryption keys toproduce a subset of encrypted encoded data slices. The processing moduleencrypts the entire data chunk slice grouping at once or encrypts eachencoded data slice one at a time using a common encryption key for allencoded data slices or a different encryption key for each encoded dataslice. The method continues at step 594 where the processing moduleidentifies storage units of a dispersed storage network (DSN) requiringa higher security than other storage units of the DSN. The identifyingmay be based on one or more of a lookup, a request, a query, and anerror message.

The method continues at step 596 where the processing module selects thesubset of encoded data slices as being targeted for storage in thestorage units requiring higher security. The selecting may be based onone or more of an accessibility level, a predetermination, a lookup, anerror message, and intrusion detection susceptibility level, and arequest. The method continues at step 598 where the processing moduleoutputs the subset of encrypted encoded data slices to the DSN forstorage therein. The method continues at step 600 where the processingmodule outputs remaining encoded data slices of the set of encoded dataslices to the DSN for storage therein.

FIG. 48D is a schematic block diagram of a decoder system. The datadecoder system includes a dispersed storage and task (DST) executionunit 1 of a set of DST execution units 1-n that is operable to retrieveencrypted chunk 1 slices, obtain partial tasks 610 associated with theencrypted chunk 1 slices, decrypt the encrypted chunk 1 slices toproduce chunk 1 slices, and execute one or more of the partial tasks 610on the chunk 1 slices to produce partial results. The DST execution unit1 includes a slice memory 602, a computing task queue 604, a chunk 1 keygenerator, a chunk 1 encryptor, and a distributed task (DT) executionmodule 606.

The computing task queue 604 may be implemented using a memory deviceand is operable to receive and store the partial tasks 610 and receive amaster key. The slice memory 602 is operable to receive and store theencrypted chunk 1 slices, chunk 1 slice names, and the master key 612.The chunk 1 key generator is operable to recover a key set 1 based onthe master key 612 and the chunk 1 slice names. The regeneratingincludes retrieving the chunk 1 slice names from the slice memory 602and retrieving the master key 612 from at least one of the slice memorysix are to and the computing task queue 604.

The regenerating includes transforming the master key 612 and a portionof the chunk 1 slice names utilizing a deterministic function to producethe key set 1. The portion of the corresponding chunk 1 slice namesincludes at least one of a pillar number associated with chunk 1 (e.g.,pillar 1), a vault ID, a segment number, a block number, an objectnumber, generation number, and a slice index. For example, the chunk 1key generator applies an exclusive OR (XOR) function on the master keysix and 12 and the pillar number to produce an interim result andapplies a mask generating function to the interim result to produce acommon key as the key set 1. As another example, for each slice of theencrypted chunk 1 slices, the chunk 1 key generator applies the XORfunction on the master key 612 and the segment number of the encryptedslice to produce an interim result and applies a mask generatingfunction to the interim result to produce a corresponding key of the keyset 1.

The chunk 1 decryptor is operable to decrypt the encrypted chunk 1slices utilizing the key set 1 to produce chunk 1 slices. For example,the chunk 1 decryptor decrypts a first encrypted slice of the encryptedchunk 1 slices utilizing a first key of the key set 1 to produce a firstslice of the chunk 1 slices. As another example, the chunk 1 decryptordecrypts each encrypted slice of the encrypted chunk 1 slices utilizinga common key of the key set 1 to produce the chunk 1 slices.

DT execution module 606 is operable to obtain the partial tasks 610 fromthe computing task queue 604, obtain the chunk 1 slice names from theslice memory 602, obtain the chunk 1 slices from the chunk 1 decryptor,and execute one or more of the partial tasks 610 on one or more of thechunk 1 slices to produce partial results 614. In addition, the DTexecution module 606 may output the partial results 614 to a requestingentity.

FIG. 48E is a flowchart illustrating an example of decrypting slices.The method begins at step 616 where a processing module (e.g., of adispersed storage and task (DST) execution unit) obtains a master key(e.g., retrieves from memory, receives from an encoding system, recoversfrom a dispersed storage network). The method continues at step 618where the processing module obtains a chunk of encrypted chunk slices,where the chunk includes one or more slices. The obtaining includes atleast one of receiving from an encoding system and retrieving from aslice memory. The method continues at step 620 where the processingmodule obtains chunk slice names corresponding to each encrypted chunkslice of the chunk. The obtaining includes at least one of receivingfrom the encoding system and retrieving from the slice memory.

The method continues at step 622 where the processing model regeneratesa key set based on the chunk slice names and the master key. Theregeneration may be in accordance with a key generation scheme, wherethe key generation scheme indicates whether to utilize a common key ofthe key set or an individual key of the key set for each slice of thechunk of encrypted chunk slices. For each key of the key set, theregenerating includes performing a deterministic function on one or moreof a portion of a slice name and the master key to regenerate the key.

The method continues at step 624 where the processing module decryptsthe encrypted chunk slices utilizing the key set to produce a chunk ofchunk slices. For each encrypted slice, the decrypting includesdecrypting the encrypted slice utilizing a corresponding key of the keyset to produce a corresponding slice of the chunk slices. The methodcontinues at step 626 where the processing module obtains partial tasks.The obtaining includes retrieving the partial tasks from a computingtask queue and receiving the partial tasks from a DST client module. Themethod continues at step 628 where the processing module executes thepartial tasks on the chunk of chunk slices in accordance with the chunkslice names to produce partial results.

FIG. 49A is a diagram illustrating an example of identifying storedchunks within a distributed storage and task (DST) execution unit 1memory of a set of DST execution unit memories 1-n. The DST executionunit 1 memory includes storage of a plurality of chunks and a chunkstorage location table 630. Each chunk of the plurality of chunksincludes at least one slice. For example, a chunk 1 of a chunkset 1includes a slice a, a slice b, and a slice c. Each chunk of theplurality of chunks is associated with a unique chunk identifier (ID).Each chunk ID includes a number of bits of a chunk ID field. Forexample, the chunk ID field includes 48 bytes when over 10{circumflexover ( )}115 unique chunk identifiers are required to provide a systemsecurity improvement. For instance, the chunk 1 of the chunkset 1 isassociated with a chunk ID of F4A7310B58 when the chunk ID is 40 bits inlength. Each slice of each chunk is associated with a unique slice name.For each chunk, the chunk storage location table six and 30 is utilizedto store a corresponding chunk entry. Each chunk entry includes a chunkID of the chunk and for each slice of one or more slices associated withthe chunk, a slice name and a slice storage location. The slice storagelocation includes an indicator as to where a slice associated with theslice name is stored within the DST execution unit memory (e.g., amemory device ID, an offset within a memory device of the memory deviceID, an address within the memory device, a disk sector, a module ID).The chunk storage location table six and 30 may be populated withentries when one of more slices of one or more chunks are received forstorage within the DST execution unit memory

FIG. 49B is a diagram illustrating an example of a chunk storagelocation table six and 30 that includes a plurality of chunk entriescorresponding to a plurality of chunks stored within a distributedstorage and task execution unit memory. Each chunk entry of theplurality of chunk entries includes a chunk identifier (ID) field 632, aslice name field 634, and a slice storage location field 636. The chunkID field 632 includes a chunk ID entry corresponding to a chunk of thechunk entry. The slice name field 634 includes one or more slice nameentries corresponding to one or more slices associated with the chunk ofthe chunk entry. The slice storage location field 636 includes acorresponding one or more slice storage location entries that correspondto the one or more slices associated with the chunk of the chunk entry.For example, a chunk associated with a chunk ID of F4A7310B58 includesthree slices with corresponding slice names of a, b, and c. A slice ofthe three slices that corresponds to the slice name of a is stored at aslice storage location of F528, a slice of the three slices thatcorresponds to the slice name of b is stored at a slice storage locationof F560, and a slice of the three slices that corresponds to the slicename of c is stored at a slice storage location of F5E0.

The chunk storage location table 630 may be utilized to facilitate sliceaccess based on a chunk ID. For example, a partial task request includesa partial task and a chunk ID to identify slices for performing thepartial task. In an example of operation, a partial task request isreceived, a received chunk ID is extracted, and the received chunk ID iscompared to one or more chunk IDs within the chunk storage locationtable 630 to determine whether a corresponding chunk is stored within anassociated DST execution unit. When the received chunk ID matches atleast one of the one or more chunk IDs within the chunk storage locationtable 630, slices associated with the chunk ID are retrieved fromcorresponding slice storage locations and a partial task of the partialtask request is performed on the slices to produce partial results. Whenthe received chunk ID does not match at least one of the one or morechunk IDs within the chunk storage location table, an alternativepartial result is generated. The alternative partial result includes atleast one of an error message, random data, and a response codeindicating that the chunk ID is not stored within the DST executionunit.

FIG. 49C is a flowchart illustrating an example of processing a partialtask request. The method begins with step 638 where a processing module(e.g., of a dispersed storage and task (DST) execution unit) receives achunk storage request that includes a chunk identifier (ID). The chunkstorage request includes one or more of the chunk ID, one or moreslices, and one or more slice names corresponding to the one or moreslices. The method continues at step 640 where the processing modulestores the chunk. The storing includes determining one or more storagelocations (e.g., within the DST execution unit) for the one or moreslices and storing the one or more slices at the one or more storagelocations.

The method continues at step 642 where the processing module updates achunk storage location table to include the chunk ID. For each slice ofthe one more slices, the chunk storage location table is updated toinclude the chunk ID, a slice name corresponding to the slice and astorage location corresponding to storage of the slice. The methodcontinues at step 644 where the processing module receives a partialtask execution request that includes a requested chunk ID and a partialtask.

The method continues at step 646 where the processing module determineswhether the requested chunk ID substantially matches a chunk ID of thechunk storage location table. The determining includes accessing thechunk storage location table and comparing each chunk ID stored in thechunk storage location table with the requested chunk ID. The methodbranches to step 650 when the processing module determines that therequested chunk ID substantially matches the chunk ID of the chunkstorage location table. The method continues to step 648 when theprocessing module determines that the requested chunk ID does notsubstantially match the chunk ID of the chunk storage location table.The method continues at step 648 where the processing module executes analternative partial task sequence. The alternative partial task sequenceincludes at least one of generating and sending an error message to arequesting entity, requesting authentication of the requesting entity,and generating a random partial result and sending the random partialresult to the requesting entity.

The method continues at step 650 where the processing module executesthe partial task on a chunk corresponding to the chunk ID to produce apartial result when the requested chunk ID substantially matches thechunk ID of the chunk storage location table. The executing includes oneor more of identifying one or more slices corresponding of the chunk(e.g., extracting slice names from an entry of the chunk storagelocation table corresponding to the chunk ID), retrieving each of theone or more slices (e.g., by identifying slice storage locationscorresponding to the one or more identified slices and retrieving theone or more slices from the identified slice storage locations), andexecuting the partial task on the one more slices in accordance with thepartial task to produce a partial result. The method continues at step652 where the processing module outputs the partial result (e.g., sendsthe partial result to the requesting entity).

FIG. 50A is a schematic block diagram of another embodiment of adistributed storage and task (DST) execution unit 654 that includes acontroller 656 and a plurality of memory devices 1-D. Each memory deviceof the plurality of memory devices 1-D includes a distributed task (DT)execution module 658, a hardware controller 660, a head 662, and servos664 when the memory device is operational to store and retrieve datautilizing at least one of a magnetic medium (e.g., hard disc) and anoptical medium (e.g., a Blu-Ray disc).

The controller 656 functions to receive slice access requests 666 andpartial tasks 668 from a network, to facilitate processing of the sliceaccess requests 666 and partial tasks 668 by one or more DT executionmodules of the plurality of memory devices 1-D to produce slices 670 andpartial results 672, to receive the slices 670 and partial results 672from the plurality of memory devices 1-D, and to output the slices 670and partial results 672 to the network. The controller 656 may beimplemented utilizing one or more computing cores.

For each memory device of the plurality of memory devices 1-D, acorresponding DT execution module 658 functions to control the at leastone of the magnetic medium and the optical medium and to process one ormore partial tasks 668 associated with the memory device. Thecontrolling includes facilitating storage of one or more slices 670assigned to the memory device within the at least one of the magneticmedium and the optical medium and facilitating storage of one or morepartial tasks 668 assigned to the DT execution module 658 correspondingto one or more slices 670 within the at least one of the magnetic mediumand the optical medium. The processing of the one or more partial tasks668 includes facilitating retrieval of at least one slice assigned tothe memory device, retrieving a corresponding partial task 668 assignedto the DT execution module 658, performing the partial task 668 on theat least one slice to produce a partial result 672, and outputting thepartial result 672 to the controller 656.

The DT execution module 658 is further operable to control the at leastone of the magnetic medium and the optical medium by generating data 674for the head 662 and position information 676 for the hardwarecontroller 660 based on storage location information for the data 674.The data 674 includes one or more of a slice, a slice name, a chunk, achunk identifier (ID), slice location table information, and a partialtask. The position information 676 includes at least one of a driveidentifier (ID) and sector numbers associated with the at least one ofthe magnetic medium and the optical medium. The DT execution module 658is further operable to control the at least one of the magnetic mediumand the optical medium by receiving data 674 from the head 662 andposition information 676 from the hardware controller 660. The hardwarecontroller 660 is operable to convert position information 676 intocontrol signals 678 (e.g., disk speed, head position) to operate theservos 664. The servos 664 operate disk drive technology includingspinning a disc past the head 662 and moving a position of the head 662.The head 662 is operable to convert data 674 into magnetic or opticalsignals for transfer to a disk and detects magnetic or optical signalsfrom the disk to convert into data 674.

The DT execution module 658 is further operable to access a slicelocation table to store and retrieve slice table information to furtherfacilitate storing data 674 and retrieving data 674. The slice tableinformation includes one or more of slice names, memory device IDs,drive IDs, and position information. For example, the slice locationtable may include slice table information such that an entry indicatesthat a data slice associated with slice name 457 is stored at memorydevice 1_2, drive 3, at sector 7,050 through sector 11,600. The DTexecution module 658 may store the slice table information in memory ofthe memory device and an internal memory associated with the DTexecution module 658.

In a slice storage example of operation, the controller 656 receives theslice and forwards the slice to a DT execution module 658 of a memorydevice 2 when memory device 2 is associated with a slice namecorresponding to the slice. The DT execution module 658 receives theslice from the controller 656 and accesses the slice location table toidentify an available position within a disc associated with memorydevice 2. The DT execution module 658 creates a new slice location tableentry that includes one or more of the slice name, integrity informationof the slice, a memory device ID, a drive ID, and position information.The DT execution module 658 stores the new slice location table entry inthe slice location table. The DT execution module 658 outputs the sliceas data 674 to a head 662 of memory device 2 and outputs positioninformation 676 corresponding to the available position to a hardwarecontroller 660 of memory device 2. The hardware controller 660 producescontrol signals 678 based on one or more of the position information 676and current position information 676 interpreted from a control signal678 of a current position of the head 662 to operate servos 664 to spinthe disc past the head 662 such that the head 662 writes the slice asdata to the disc to store the slice. A partial task may be stored in asimilar manner. Subsequent retrieval of the slice and partial task maybe accomplished in a similar manner reversing the order of the stepsdescribed above. For example, receive a retrieval request, access theslice location table to identify a storage location, control the servosfor the location, read the slice or partial task as data from the head.Next, the DT execution module 658 may perform the partial task on theslice to produce the partial result 672.

FIG. 50B is a flowchart illustrating another example of processing apartial task request. The method begins at step 680 where a processingmodule (e.g., of a controller of a dispersed storage and task (DST)execution unit) receives a partial task requests that includes one ormore partial tasks associated with a plurality of slices. The requestmay include one or more partial tasks, the plurality of slices, slicenames associated with the plurality of slices, and a chunk identifier(ID) corresponding to the plurality of slices.

For each slice of the relative slices to be processed with at least onepartial task of the one or more partial tasks, the method continues atstep 682 where the processing module selects a memory device to executethe processing of the slice. The selecting includes at least one ofselecting a memory device such that the plurality of slices are alreadystored on the memory device and selecting an available memory devicewhen the plurality of slices have not been stored yet. For example, theprocessing module selects the available memory device as a memory devicewith sufficient storage space to store the plurality of slices.

For each slice to be processed, the method continues at step 684 wherethe processing module sends the at least one partial task of the one ormore partial tasks to the memory device. The sending includes outputtingthe slice to the memory device when the slice has not been previouslystored in the memory device. For each slice to the process, the methodcontinues at step 686 where the processing module receives at least onepartial result from the memory device

FIG. 50C is a flowchart illustrating another example of processing apartial task request. The method begins at step 688 where a processingmodule (e.g., of a distributed task (DT) execution module of a memorydevice of a dispersed storage and task (DST) execution unit) receives aslice for storage and partial task processing. The method continues atstep 690 where the processing module stores the slice in a memoryutilized to store a plurality of slices. The method continues at step692 where the processing module receives at least one partial taskassociated with at least one slice of the plurality of slices. Themethod continues at step 694 where the processing module identifies aslice associated with the at least one partial task. For example, theprocessing module extracts a slice name from the partial task. Asanother example, the processing module extracts a chunk identifier (ID)from the partial task.

The method continues at step 696 where the processing module retrievesthe slice associated with the at least one partial task to produce aretrieved slice. For example, the processing module controls a memorydevice servo to access the slice via a head of the memory device. Themethod continues at step 698 where the processing module executes the atleast one partial task on the retrieved slice to produce a partialresult. The method continues at step 700 where the processing moduleoutputs the partial result. For example, a processing module sends thepartial result to a controller of the DST execution unit. As anotherexample, the processing module outputs the partial result to arequesting entity via the controller of the DST execution unit.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc. that may usethe same or different reference numbers and, as such, the functions,steps, modules, etc. may be the same or similar functions, steps,modules, etc. or different ones.

While the transistors in the above described figure(s) is/are shown asfield effect transistors (FETs), as one of ordinary skill in the artwill appreciate, the transistors may be implemented using any type oftransistor structure including, but not limited to, bipolar, metal oxidesemiconductor field effect transistors (MOSFET), N-well transistors,P-well transistors, enhancement mode, depletion mode, and zero voltagethreshold (VT) transistors.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for storage processing, the methodcomprises: identifying, by a storage unit, a data slice for processingbased on a corresponding partial task; determining whether the dataslice is locally available; in response to a determination that the dataslice is not locally available, determining whether a redundant dataslice is available from another storage unit; in response to adetermination that the redundant data slice is not available fromanother storage unit, facilitating rebuilding the data slice to producea rebuilt data slice by: retrieving a decode threshold number of dataslices corresponding to the data slice; decoding the decode thresholdnumber of data slices to reproduce a data segment; re-encoding the datasegment to produce a pillar width number of data slices that includesthe rebuilt data slice; storing locally one of: the rebuilt data sliceor the redundant data slice; and processing one of: the data slicelocally available, the rebuilt data slice stored locally, or theredundant data slice stored locally in accordance with the correspondingpartial task to produce a partial result.
 2. The method of claim 1,wherein the identifying includes at least one of: retrieving the partialtask for execution, extracting data slice metadata from the partialtask, extracting a chunk identifier (ID) from the partial task, orobtaining data slice metadata based on the chunk ID.
 3. The method ofclaim 2, wherein the obtaining data slice metadata of the data slicebased on the chunk ID includes a table lookup.
 4. The method of claim 1,wherein the determining whether the data slice is locally available isbased on one or more of: issuing a local read data slice request andreceiving a local read data slice response, a local storage tablelookup, and receiving a local data slice name list.
 5. The method ofclaim 1, wherein the determining whether a redundant data slice isavailable from another storage unit is based on at least one of: aretrieval attempt, a query, a redundant data slice location table, anddata slice metadata extracted from the partial task.
 6. The method ofclaim 5, wherein the retrieval attempt includes obtaining a distributedstorage execution unit ID associated with another storage unit.
 7. Themethod of claim 5 further comprises, when the redundant data slice isavailable, retrieving the redundant data slice from the another storageunit by generating a data slice retrieval request, sending the dataslice retrieval request to the other storage unit based on the otherdistributed storage execution unit ID, and receiving the redundant dataslice.
 8. The method of claim 1, wherein the facilitating includes atleast one of utilizing a rebuilding process and utilizing azero-information gain (ZIG) rebuilding process.
 9. The method of claim1, wherein the retrieving the decode threshold number of data slicescorresponding to the data slice includes generating at least a decodethreshold number of read data slice requests, sending the at least thedecode threshold number of read data slice requests to other storageunits, and receiving the at least the decode threshold number of dataslices corresponding to the data slice.
 10. The method of claim 1,wherein the decoding includes applying an exclusive OR operation. 11.The method of claim 1, further comprising: retrieving a decode thresholdnumber of partial data slices, wherein the decoding includes generatingat least a decode threshold number of zero-information gain (ZIG)partial data slice requests and sending the at least the decodethreshold number of ZIG partial data slice requests to another storageunits, wherein each of the another storage units generates a ZIG partialdata slice, and receiving the at least a decode threshold number of ZIGpartial data slices.
 12. The method of claim 11, wherein the generatingof a ZIG partial data slice by a storage unit of the another storageunits includes reducing a generator matrix to produce a square matrixthat exclusively includes rows identified in the partial data slicerequest, inverting the square matrix to produce an inverted matrix toproduce a vector, and matrix multiplying the vector by a row of thegenerator matrix corresponding to the data slice to be rebuilt, toproduce the ZIG partial data slice.
 13. A storage network comprises: afirst computing device with processing circuitry configured to executeoperational instructions to: identify, by a storage unit, a data slicefor processing based on a corresponding partial task; determine whetherthe data slice is locally available; for data slices not availablelocally, determine whether a redundant data slice is available fromanother storage unit; for redundant data slices not available fromanother storage unit, facilitate rebuilding the data slice to produce arebuilt data slice by: retrieving a decode threshold number of dataslices corresponding to the data slice; decoding the decode thresholdnumber of data slices to reproduce a data segment; and re-encoding thedata segment to produce a pillar width number of data slices thatincludes the rebuilt data slice; and store locally one of: the rebuiltdata slice or the redundant data slice; and process one of: the dataslice locally available, the rebuilt data slice stored locally, or theredundant data slice stored locally in accordance with the correspondingpartial task to produce a partial result.
 14. The storage network ofclaim 13, wherein the data slice is identified by at least one of:retrieving a partial task for execution, extracting data slice metadatafrom the partial task, extracting a chunk identifier (ID) from thepartial task, and obtaining a data slice metadata based on the chunk ID.15. The storage network of claim 13 further comprises, when theredundant data slice is available, retrieving the redundant data slicefrom the another storage unit by generating a data slice retrievalrequest, sending the data slice retrieval request to the another storageunit based on an ID of the another storage unit, and receiving theredundant data slice.
 16. The storage network of claim 13, wherein theretrieving the decode threshold number of data slices corresponding tothe data slice includes: generating at least a decode threshold numberof read data slice requests, sending the at least the decode thresholdnumber of read data slice requests to other distributed storageexecution units, and receiving the at least the decode threshold numberof data slices corresponding to the data slice.
 17. A method for storageprocessing, the method comprises: receiving data and a correspondingtask; selecting one or more storage units for the task based on acapability level associated with each of the storage units; determiningprocessing parameters of the data based on a number of storage units;determining task partitioning based on the storage units and theprocessing parameters; processing the data in accordance with theprocessing parameters to produce data slice groupings; partitioning thetask based on the task partitioning to produce partial tasks; selectingone or more data slices of the data slice groupings in accordance with aredundancy scheme to produce one or more redundant data slices;determining pillar mapping for the data slice groupings and the one ormore redundant data slices based on a partial task execution requirementand a storage reliability requirement; sending the data slice groupingsand corresponding partial tasks to the one or more storage units inaccordance with the pillar mapping; and sending the one or moreredundant data slices to at least one storage unit of the one or morestorage units in accordance with the pillar mapping.
 18. The method ofclaim 17, wherein the selecting one or more data slices of the dataslice groupings is based on a redundancy scheme adapted to produce oneor more redundant data slices, wherein the redundancy scheme is based onone or more of: a reliability requirement, an access latencyrequirement, a storage unit performance level, or a storage unitreliability level.
 19. The method of claim 18, wherein the determiningthe pillar mapping includes storing the one or more redundant dataslices in a storage unit associated with a favorable performance leveland a favorable available storage capacity level.
 20. The method ofclaim 17, wherein the sending includes outputting the data slicegroupings and corresponding partial tasks in accordance with taskexecution ordering.